Fabric Influencers Spotlight: August 2026
Explore the August Fabric Influencers Spotlight featuring community-driven guidance, tutorials, AI use cases, and Power BI insights from Microsoft MVPs and Fabric Super Users. Discover practical examples of Microsoft Fabric in action across governance, data engineering, analytics, real-time intelligence, and AI.Fabric August 2026 Feature Summary
Welcome to the August 2026 Fabric update! Microsoft Fabric continues to evolve with new capabilities that help organizations build, manage, and scale their data and AI solutions more efficiently. This month's updates introduce enhancements across Fabric Platform, OneLake, Data Engineering, Conversational Analytics, Data Warehouse, Real-Time Intelligence, and Data Factory. Whether you're strengthening governance, improving performance, streamlining development workflows, or expanding AI-powered experiences, these updates are designed to help you get more value from your data while simplifying day-to-day operations. Explore the following highlights to see what's new in Microsoft Fabric this month. Events and Announcements Join us for FABCON and SQLCON in Barcelona, September 28 – October 1, 2026 Explore what’s possible with Microsoft Fabric and get up to speed on the latest in SQL, analytics, and AI. From 130 sessions and 4 keynotes to workshops, the expo, community spaces, and the Power BI DataViz World Championships, this is where the data community comes together. Learn directly from Microsoft and community experts shaping the future of Fabric and SQL. Register now and save €200 with code FABCMTY200. Fabric Platform KQL-Dashboard Embed in Fabric (Preview) A new capability that lets you add interactive KQL-Dashboard content directly into your own browser-based web applications is available. Now, you can bring Fabric analytics into the apps, portals, and workflows your users already use. Analytics are most useful when they are available where decisions happen. With Fabric Embed, you can place Fabric content inside a custom application or internal portal instead of requiring users to switch to the Fabric portal. Fabric Embed can help you: Bring interactive analytics into an existing business workflow. Explore Fabric analytics without switching between your application and the Fabric portal. Keep Fabric workspace permissions and Microsoft Entra ID identities at the center of access control. Build user-based embedded experiences for people who already have access to the underlying Fabric item. The embedded experience complements the Fabric portal. Content owners can continue to create and manage analytics in Fabric while application developers present that content in the context most useful to their users. To learn more, refer to the Microsoft Fabric Embed documentation. Git Integration – Workspace Relation API (Preview) Following our recent announcement of branch workspaces and the relationship that’s automatically created when a user performs a branch-out operation, we’re introducing the new Workspace Relations API. These endpoints let you create, query, update, and delete relationships between Fabric workspaces programmatically — and for anyone building automation around Git integration, branched workspaces, and CI/CD, it’s a big deal! The workflow is straightforward. A developer creates a feature branch. An ADO pipeline or GitHub Action then provisions a new feature workspace, configures Git, applies the right settings and permissions, and synchronizes the workspace. Finally, the Workspace Relations API links that feature workspace back to its parent — closing the loop and giving your automation a first-class, queryable connection between parent and branched workspaces. Figure: Branch workspace relation. To learn more, refer to Development process using Branch-Out experience. Git Integration and Deployment Pipeline – Item level permission restriction Starting December 1, 2026, users without read-write permissions on workspace items can't use Git integration and won’t be able to deploy a workspace or assign workspace to a stage via Deployment Pipeline. This restriction can result in loss of access to certain items because of sensitivity labels and protection policies applied to those items. To learn more, refer to Information Protection in Microsoft Fabric. OneLake Resource instance rules for OneLake (Generally Available) Resource instance rules for OneLake are ready for production workloads across enterprise analytics environments. They give workspace admins a precise way to allow access from trusted Azure resource instances while continuing to enforce network and data-level protections. Use resource instance rules when you need to enable secure service-to-service access to OneLake without relying solely on IP allowlists or requiring private networking for every integration. Combined with Private Link, IP firewall rules, and identity-based permissions, they help organizations apply layered security based on the needs of each workspace and workload. Resource instance rules support a broad set of Azure services that can present a verifiable Azure resource identity, including Azure Databricks, Azure SQL Server, Azure Data Factory, Azure Event Grid, Azure Machine Learning, and more. Configuration requires only the Azure resource ID, simplifying setup while maintaining control over which Azure resources can access OneLake. To learn more, refer to Manage inbound access to OneLake with Resource Instance Rules. Data Engineering Native Execution Engine performance improvements This month, we continued to improve the Native Execution Engine (NEE) with a new set of query execution optimizations designed to accelerate Spark workloads while reducing compute consumption. Key enhancements include broadcast joining reuse across queries, native acceleration for ranking window functions such as RANK and DENSE_RANK, and automatic materialization of reused Common Table Expressions (CTEs). Together, these optimizations eliminate redundant computation, keep more processing within the engine's vectorized execution path, and improve performance for common data engineering patterns including large joins, analytical reporting, ranking workloads, and complex transformation pipelines. Because these improvements are enabled automatically when NEE is available, customers benefit from faster execution times, lower Fabric capacity consumption, and improved price-performance without requiring code changes to existing notebooks, Spark Job Definitions, or pipelines. These optimizations are enabled by default once the Native Execution Engine is turned on, allowing customers to realize performance gains immediately without additional configuration or tuning. Customers can enable NEE at the workspace environment level by navigating to Environment > Acceleration and turning on Native Execution Engine, ensuring it is available for all Spark sessions using that environment. It can also be enabled at the session level by setting spark.native.enabled=true in Spark configuration. With NEE enabled, customers can seamlessly take advantage of the latest runtime innovations to process data faster, improve resource efficiency, and maximize the value of their Fabric capacity investments. To learn more about Native Execution Engine explore our documentation Native execution engine for Fabric Data Engineering. Fabric Runtime 2.0 (Generally Available) As the execution foundation for Microsoft Fabric's Data Engineering and Data Science experiences, Runtime 2.0 delivers a modern, high-performance platform built on Apache Spark and deeply integrated across the Fabric ecosystem. Purpose-built for large-scale data processing and analytics workloads, Runtime 2.0 represents a major advancement in performance, reliability, security, and future readiness. Built on the latest open-source innovations, it enables customers to accelerate data processing, simplify operations, and take advantage of the newest capabilities across Microsoft Fabric. This release includes significant platform upgrades, including Apache Spark 4.1, Delta Lake 4.2, Python 3.13, Java 21, Scala 2.13, and Azure Linux 3.0, providing a modern and enterprise-ready foundation for the next generation of data engineering, data science, and analytics workloads. These enhancements enable customers to take advantage of the latest open-source innovations while continuing to benefit from a fully managed, enterprise-grade experience in Microsoft Fabric. Whether you're building data pipelines, developing AI and machine learning solutions, processing streaming workloads, or powering enterprise analytics, Runtime 2.0 provides a more capable, scalable, and performant platform for your workloads. With improved performance, updated open-source foundations, and continued investment in capabilities such as the Native Execution Engine, Runtime 2.0 provides a modern platform for data engineering, data science, and analytics workloads in Microsoft Fabric. Explore the full documentation and start using Runtime 2.0 in production Runtime 2.0 in Fabric. Enhanced Spark Properties Support in Notebook and Spark Job Definition Notebook and Spark Job Definition (SJD) activities now enable users to specify Spark properties directly within the data integration pipeline. This enhancement allows Spark properties to be set inside the activity panel, ensuring that the values provided are used for activity execution. If an Environment item is linked to the Notebook or SJD and both the activity panel and Environment item define the same property, the value specified in the activity panel will take precedence and overwrite the Environment value. In the case of Notebooks, if the %%configure command is used within the notebook code to set a Spark property, the value set using %%configure will be applied for execution. This update offers users maximum flexibility, allowing Spark properties to be defined at different layers based on their specific use cases. By supporting property configuration in the activity panel, Environment item, and notebook code, users can tailor property values to meet the unique requirements of each execution of Notebook/SJD. To learn more on Transform data by running notebook and Transform data by running a Spark Job Definition activity. Conversational Analytics Enhanced Data Agent Visualizations with Fabric Visuals The data agent now uses Fabric visuals to render the charts it returns, bringing higher-quality, more consistent visualizations into your conversations with your data. When you ask a question like "Generate a bar chart of revenue by region" or "Show me my top 10 customers by sales," the data agent responds with an interactive, polished visual alongside its text and table answers, so you can spot trends, comparisons, and outliers. Because the data agent now shares the same visual foundation as Fabric Apps, charts look and behave consistently with AI-generated visuals in Fabric, with refinements to formatting, legends, tooltips, and axis scaling. Supported chart types include line, bar, stacked bar, pie, scatter, and area charts. To learn more, refer to the Get visual responses from a Fabric data agent documentation. Advanced DAX Generation for Semantic Models in Data Agents Advanced DAX generation for Power BI semantic models is now available in Fabric data agents when you use the Preview runtime. Instead of generating a DAX query in a single pass, the new system works iteratively as a specialized sub-agent that can use tools, inspect results, and refine its approach across multiple steps, providing significant improvements in response accuracy. It also uses instance value indexing to resolve values from the semantic model before generating a query, resulting in more accurate and reliable filters. This update is built on the same semantic-model query engine used across Fabric Skills, Power BI, and M365 Copilot, providing more consistent answers across Microsoft experiences. To use the new experience, open the Runtime dropdown in the data agent ribbon and switch from Standard to Preview. More improvements for semantic models in data agents are coming soon, including data source description and instructions, granular schema selection, and example queries. In the Preview runtime, the data agent uses advanced DAX generation to answer a question over a connected semantic model, enabling more accurate DAX generation and responses. To learn more, refer to the Semantic model best practices for data agent documentation. Data Agent orchestrator upgraded to GPT 5.1 The data agent orchestrator now runs on GPT-5.1, across both the standard and preview runtimes. The orchestrator handles how questions are rephrased, how work is planned across your data sources, and how the final answer is composed — so this upgrade changes behavior in all three. Most of what you see should be an improvement in answer quality and planning, but the change is not behavior-neutral: prompts tuned against the previous model may produce different results. We recommend re-running your evaluations, reviewing the results against your saved baselines, and updating your agent instructions and prompts where the new behavior doesn't match what your scripts expect. To learn more, refer to the data agent runtimes documentation. Example Query Usability Improvements in Data Agent We've made several usability improvements, for example queries. Errors now surface inline, directly alongside the query, so you can identify and correct issues without leaving the editor. The editor also resizes automatically based on the length of your query, removing the need to adjust the pane manually as you write. To learn more, refer to the example queries in data agent documentation. Add Schema Descriptions for SQL Sources in Data Agent Users can now provide tailored schema descriptions through the new schema description editor, available for SQL sources on the Preview runtime. Schema descriptions improve query generation and accuracy by giving the agent more context about what everything in your data means — use them to resolve ambiguous columns, or to give a table or field a more precise meaning than its name conveys. Instead of inferring intent from column names alone, the agent works from what your data team documented, so it selects the right tables and interprets fields the way you intended. To learn more, refer to the schema descriptions documentation. Data Agent is migrating from Assistants API to Responses API The OpenAI Assistants API that powers the orchestration layer for the Microsoft Fabric data agent, is currently scheduled to be shut down by OpenAI on August 26, 2026. After that date, direct calls to the Assistants API will stop working. If you connect to a Fabric data agent programmatically through the Assistants API, you need to migrate to the data agent Model Context Protocol (MCP) endpoint. SDK and Fabric portal users require little or no action because Microsoft will migrate those experiences internally, although conversation history may reset once. Existing agent data sources, instructions, and tools remain unchanged. To learn more, refer to Prepare your Fabric Data Agent integrations for Assistants API retirement. Fabric Data Agents in Microsoft Copilot Studio (Generally Available) Now, you can bring governed business data from Microsoft Fabric into Copilot Studio agents, so those agents can answer questions and support business processes using trusted enterprise data. Since preview, the integration has moved to the new tool-based experience: select Add a tool, search for Fabric, and add Fabric IQ Data MCP, and your agent can call the Fabric data agent like any other tool. The Fabric data agent still runs in Fabric and respects permissions on the underlying data sources. You can also publish your agent to Microsoft Teams and Microsoft 365 Copilot, so business users get data-grounded answers where they already work. To learn more, refer to the Fabric Data Agent MCS GitHub documentation for setup steps and join the community discussion to share feedback. Fabric data agents in Microsoft Foundry: Easier to connect, easier to trust Fabric data agents in Microsoft Foundry are now easier to connect and easier to monitor. The integration moves to Model Context Protocol, so your Fabric data agents appear as tools that Foundry agents can invoke when they need enterprise data in OneLake. Connecting them no longer means hunting for workspace and artifact IDs. You add the Fabric IQ (OneLake Catalog) tool, filter for data agents, and pick the ones you want by name. You can also connect more than one Fabric data agent to a single Foundry agent, so an agent can draw on a sales agent, a supply chain agent, and a customer support agent and choose the right one for the question. On the operations side, you can now view logs and traces for Fabric data agents through Foundry Observability. Traces show which tools were invoked, how long each step took, and what came back, which makes it much easier to troubleshoot an answer that looks wrong or a workflow that runs slow. This is the visibility teams need to move agents from experiments to production. The update is rolling out to all regions over the coming days. Add Fabric data agent as part of Fabric IQ to your Foundry agent. To learn more, refer to the Observability for Fabric data agents in Microsoft Foundry documentation. Add co-publishers for data agents in Microsoft 365 Copilot When you publish a Fabric data agent to Microsoft 365 Copilot, the Microsoft 365 agent platform registers the agent and records you as its only owner. That created a problem for teams. Your co-creators could still edit the data agent in Fabric, but when they tried to republish it, the operation failed, because Microsoft 365 only lets registered owners publish. Fabric access and Microsoft 365 ownership are two separate lists, so giving someone edit rights in Fabric was never enough. Now, you can avoid this with co-publishers. After you publish the data agent, open Settings, go to the Publishing pane, and add your Fabric co-creators under Microsoft 365 Copilot co-publishers. Each person you add is registered as a co-owner on the Microsoft 365 agent platform, so anyone on that list can republish the agent. Add co-publishers right after your first publish so no one hits a failure in the meantime. Publishing pane in the Fabric data agent settings, showing where you add Microsoft 365 co-publishers. To learn more, refer to the Consume a data agent from Microsoft 365 Copilot (preview) documentation. Data Warehouse Identity columns with identity insert (Generally Available) Since preview, thousands of customers have adopted IDENTITY to auto-generate surrogate keys and streamline migrations from SQL Server, Azure SQL Database, and Azure Synapse. Now we're introducing support for IDENTITY_INSERT and reseed operations - two highly needed additions - so you can insert explicit key values, migrate data in bulk with COPY INTO, and safely realign identity ranges with DBCC CHECKIDENT. Figure: Using identity insert on Fabric Data Warehouse. IDENTITY columns are available now in every Fabric Data Warehouse. To learn more, check our updated tutorial and documentation. CI/CD 2.0 with DacFx (Preview) Microsoft Fabric Data Warehouse is introducing a major update to the DacFx engine that powers schema comparison, Git integration, and deployment pipelines. DacFx builds a declarative model of your warehouse and determines the schema changes required to move safely between development, test, and production environments. Git-integrated CI/CD workflow for Microsoft Fabric, showing feature workspace synchronization, branch merging, and deployment pipeline promotion across development, test, and production workspaces. With this update, Git integration uses DacFx-based incremental extraction to produce cleaner, more focused commits. Deployment pipelines also use the updated model to generate more accurate comparisons and smarter deployment plans, with settings tuned for schema evolution. The new warehouse item definition version 2.0 updates the SQL project SDK, moves shared queries into a .sharedqueries folder, adds project-level Git configuration, and re-extracts object definitions to support constraints, identity columns, clustering, and consistent formatting. These changes make future commits easier to review and reduce noisy diffs. For more information, refer to the Upgrade Fabric Data Warehouse System File Version in a Git Integrated Fabric workspace documentation. Microsoft Fabric source control notification prompting users to apply the latest Warehouse system update, with a warning that the update will introduce differences between the workspace and its connected Git repository. The update also improves comparison accuracy. Git-connected workspaces can adopt the update when ready through the System update available experience, giving teams control over upgrade timing. Review and commit the generated changes before continuing normal development and deployment workflows. To learn more, refer to the Development and Deployment Overview documentation. Simplify Fabric Warehouse deployments with Schema Compare in VS Code Database deployments should not feel like a guessing game. With Schema Compare in Visual Studio Code, developers can see exactly what changed before those changes reach a Fabric Warehouse—bringing clarity and control to every release. Compare a Fabric Warehouse with another warehouse or a SQL database project, then review differences across tables, views, stored procedures, functions, and other database objects in a clear, object-by-object view. Choose the changes you want, update the project from the warehouse, or deploy selected changes to the target—without manually assembling and reviewing every deployment script. By keeping database projects synchronized in Git, teams gain a reliable source of truth and can bring schema changes into familiar pull-request and CI/CD workflows. The result is a safer, more intentional path from development to production, with fewer surprises at deployment time. Before applying changes, review the generated script for unsupported operations and potential data loss. To learn more, refer to the Develop warehouse projects in Visual Studio Code and Schema Compare in the MSSQL extension documentation. GPU Query Acceleration (Preview) Query Acceleration brings GPU-powered performance directly to Fabric Data Warehouse, enabling eligible analytical queries to run faster without query rewrites, special syntax, or additional systems to manage. Query Acceleration in Fabric Data Warehouse uses GPUs to accelerate the most compute-intensive portions of analytical queries, helping overcome the limits of CPU-only execution. It works transparently with existing T-SQL, Direct Query reports, applications, and tools, automatically offloading eligible operations such as scans, filters, joins, and aggregations to GPUs while the CPU continues to manage the rest of the execution pipeline. Customers can use Query Insights, Data Warehouse Monitoring, and SQL Server Management Studio (SSMS) for query execution plans to identify accelerated queries and understand how Query Acceleration is applied during query execution. Designed for analytical and high-concurrency workloads, Query Acceleration can improve throughput, reduce query latency, and deliver more consistent performance for dashboards and interactive analytics. Acceleration is applied selectively, enabling performance gains even when only part of a query is eligible for GPU execution. The capability is built with reliability in mind. Unsupported operations or runtime constraints can seamlessly fall back to CPU execution without affecting query correctness. Performance improvements depend on workload characteristics, but Microsoft benchmarks have demonstrated gains of up to 7× across reporting, application, and AI-driven analytics scenarios. Query Acceleration builds on Microsoft's Tensor Query Processor research, described in CoddSpeed: Hardware Accelerated Query Processing in Microsoft Fabric, which was selected as the SIGMOD Companion 2026 Best Industry Paper. To sign up for the Preview, please fill out the form. Metadata Sync supports Delta Checkpoint V2 (Generally Available) Metadata Sync (MD Sync) now supports Delta Checkpoint V2, enabling synchronization of modern Delta tables across both MD Sync (Legacy) and MD Sync (New). Delta Checkpoint V2 is a Delta Lake enhancement designed to improve scalability for large tables through a more efficient checkpoint structure. Previously, tables using Checkpoint V2 couldn't be synchronized and were reported as unsupported. With this release, MD Sync can discover and synchronize Delta tables that use the Checkpoint V2 format. This enhancement helps customers: Synchronize Delta tables that use Checkpoint V2. Improve interoperability with Spark, Databricks, and other Delta-based platforms. Support metadata synchronization for large-scale Delta tables more efficiently. Continue using existing checkpoint formats without any changes. MD Sync support for Delta Checkpoint V2 is available in both MD Sync (Legacy) and MD Sync (New), helping ensure consistent access to Delta tables across Fabric experiences. Secure data ingestion with COPY INTO and Workspace Identity (Generally Available) COPY INTO in Fabric Data Warehouse now supports Workspace Identity, enabling users to load approved data from OneLake or ADLS Gen2 without requiring direct access to the source files. Previously, ingestion users often needed permissions to both the target warehouse and the source storage location, or teams relied on SAS tokens, account keys, or service principals. With this release, source access can be centrally assigned to the workspace identity, while users retain only the SQL permissions required to load data into the target table. Key Capabilities: Load approved data without granting users direct access to raw storage. Use managed identity-based authentication for OneLake and ADLS Gen2 sources. Reduce reliance on SAS tokens, shared keys, and service principal secrets. Maintain separate authorization boundaries for source access and target-table permissions. Support least-privilege ingestion and separation of duties between storage and warehouse administrators. Workspace Identity support for COPY INTO is generally available in Fabric Data Warehouse, providing a simpler and more governed approach to secure data ingestion. To learn more, refer to the Ingest Data into Your Warehouse Using the COPY Statement and COPY INTO (Transact-SQL) documentation. SQL Audit Logs: More Signal, Less Noise with Predicate Filtering (Generally Available) SQL Audit Logs in Fabric Data Warehouse and SQL Analytics Endpoint now support identity-based predicate exclusion filtering, enabling administrators to reduce repetitive audit events generated by selected users and service principals. Previously, expected activity from automation identities, scheduled processes, metadata synchronization jobs, and other operational actors could create significant audit noise. With this release, administrators can configure exclusions through the API or SQL Audit Logs user experience, while activity from identities that do not match the exclusion predicate continues to be audited normally. Key Capabilities: Reduce repetitive audit events from known users and service principals. Focus investigations on higher-value and unexpected activity. Lower the storage, processing, export, and query burden associated with low-value events. Manage identity exclusions through either automated APIs or the user experience. Apply a governed audit policy aligned with organizational monitoring and compliance requirements. Identity-based predicate exclusion filtering is generally available for SQL Audit Logs in Fabric Data Warehouse and SQL Analytics Endpoint, providing a cleaner audit stream, less operational overhead, and more focused investigations. To learn more, refer to the SQL Audit Logs in Fabric Data Warehouse documentation. OneLake security improvements for SQL analytics endpoints (Generally Available) OneLake Security for SQL analytics endpoints now includes improvements for nested groups, shortcut-backed tables, column-level security, and service principals, enabling more consistent enforcement of OneLake security policies across enterprise Fabric environments. Previously, limitations with group expansion, shortcut scenarios, and service principal ownership could make centralized security difficult to apply at scale. With these improvements, customers can define security at the source lakehouse and rely on the SQL analytics endpoint to honor those policies across producer and consumer workspaces. Key Capabilities: Manage access through nested Microsoft Entra group hierarchies. Honor source-side OneLake Security policies for shortcut-backed tables in hub-and-spoke architectures. Apply column-level security consistently when users receive access through groups. Use service principals for automated deployments, pipelines, and application-owned data products, including service principal-owned lakehouses. Define security once in OneLake and reduce the need to duplicate permissions across consumer workspaces and Fabric engines. These OneLake Security improvements help make security synchronization more practical for enterprise architectures while providing consistent access control across lakehouses and SQL analytics endpoints. Microsoft is also continuing to improve security sync notifications, error handling, and permission propagation across Fabric experiences. To learn more, refer to the OneLake Security for SQL analytics endpoints documentation. Real-Time Intelligence Set Alerts Directly from Anomaly Detector (Generally Available) Detecting anomalies becomes more valuable if you can act on them. Previously, after publishing an anomaly detector configuration, you had to leave Anomaly Detector and navigate to Real-Time Hub to create an alert. This added extra steps and interrupted your workflow right after completing your configuration. With this update, you can now create alerts directly from Anomaly Detector. Once you publish a configuration, use the Set alert button in the ribbon to launch the alert creation pane without leaving Anomaly detector. If your configuration hasn't been published yet, you'll be guided through publishing first and then taken directly to the alert setup experience. This helps you move seamlessly from configuring anomaly detection to monitoring it in production. The integrated experience allows you to monitor your anomalies on each event, helping you get notified as soon as anomalies are detected. If you have more complex business logic, select on each event when to add in additional logic to your conditions. Whether you're monitoring operational metrics, business KPIs, or real-time telemetry, you can now complete the entire workflow in one place and start acting on detected anomalies faster with fewer clicks. Create alerts directly from your anomaly detector configuration and continue your workflow without navigating to another experience. Configure notifications for anomaly detector events directly within Anomaly Detector and start monitoring your published configuration immediately. Anomaly detector supports Eventhouse shortcut tables Anomaly Detector now supports Eventhouse shortcut tables, making it possible to analyze data without first copying or moving it into a dedicated Eventhouse table. You can create anomaly detectors directly on supported shortcut tables and use the same analysis, model recommendations, and continuous monitoring experiences available for native Eventhouse data sources. This expands anomaly detection to a broader range of data already connected through Eventhouse shortcuts, helping teams monitor external and federated data sources with less setup and duplication. By enabling anomaly detection directly on shortcut tables, you can move more quickly from connecting data to detecting issues, while continuing to work within a unified Real-Time Intelligence experience. To learn more, refer to the Anomaly Detection in Real-Time Intelligence documentation. Operations Agent Activity Log Understanding what your agent is doing and why is key to building trust and improving outcomes. The activity log is designed to provide that transparency. It gives you a clear view into the agent’s behavior, including the conditions it evaluated, the recommendations it generated, and how those recommendations were handled. Whether you are validating results, troubleshooting unexpected behavior, or refining your configuration, the activity log helps you better understand how decisions are being made. You can access the activity log from the Activity log section in the side navigation. It presents a chronological timeline of events with timestamps and relevant context for each entry. Selecting any event allows you to explore additional details and understand what happened at each step. In the Operation details page, you can view the operation details and status. To learn more, refer to the Create and Configure Operations Agents documentation. Eventstream MQTT connector (Generally Available) It is now easier than ever to ingest real-time data from MQTT brokers directly into Microsoft Fabric Real-Time Intelligence. MQTT is one of the most widely adopted messaging protocols for lightweight, low-bandwidth event driven messaging scenarios. Eventstream MQTT connector simplifies the ingestion of operational and IoT data into Microsoft Fabric, helping organizations turn real-time device events into actionable insights. Key Benefits: Connect to any MQTT broker and ingest messages directly into Fabric Eventstream. Production-ready reliability and support with General Availability readiness. Enterprise-grade security with support for TLS, mutual TLS (mTLS), and custom certificate authorities managed through Azure Key Vault. Private network connectivity through Eventstream's streaming connector virtual network capabilities, enabling secure access to brokers hosted in private and on-premises environments. To learn more, refer to the Add MQTT source to an eventstream documentation. Reference data enrichment in Eventstream (Preview) Eventstream now enables you to enrich real-time event streams with contextual business data using Reference Data Join. Simply add a Reference Data node to your Eventstream, select a Delta table from a Fabric Lakehouse, and use it to enrich streaming events with lookup, metadata, or reference information. You can also leverage Lakehouse shortcuts to access Delta tables across OneLake, making it easy to bring contextual data from anywhere in your Fabric environment into your real-time processing pipelines. Reference Data Join supports both no-code and SQL-based enrichment experiences. Use the built-in Join operator to visually configure INNER and LEFT OUTER joins or use the SQL operator for advanced scenarios. Select only the columns you need from the reference dataset and configure optional refresh intervals to keep slowly changing reference data up to date. This enables Eventstream to continuously use the latest lookup information for real-time enrichment, without requiring additional data movement or downstream processing pipelines. You can easily add multiple reference data sources to a single Eventstream and combine them with streaming data to create richer, more contextual event pipelines. Developers and data engineers can test and validate join conditions, preview join results, and verify SQL-based enrichment queries before deploying them into production, helping ensure accuracy and confidence in real-time data processing workflows. Reference Data Join unlocks powerful real-time enrichment scenarios in Eventstream. Users can enrich IoT telemetry with device metadata, correlate operational events with customer and product information, perform lookups against business reference datasets, and add contextual information to streaming data in flight. By bringing reference data and event processing together in a single experience, Eventstream enables customers to transform raw events into actionable business insights in real time. To learn more, refer to the Reference data join in Eventstream using Lakehouse documentation. Eventstream observability in Workspace Monitoring re-enabled with per-Eventstream control (Preview) Eventstream observability in Workspace Monitoring is back — now with granular control over which Eventstreams emit monitoring data. A new ‘Log Eventstream activity’ toggle in Eventstream Settings lets you enable or disable observability per Eventstream, so you can balance monitoring coverage with capacity consumption. When enabled, your Eventstream emits performance metrics, error counts, and health status to three tables in your Workspace Monitoring Eventhouse: EventStreamMetrics: throughput, backlog, and watermark delay EventStreamErrorMetrics: deserialization, conversion, and runtime error counts EventStreamNodeStatus: node health (Running / Failed) The toggle defaults to OFF for all Eventstreams. To get started, open any Eventstream, go to Settings, and turn on Log Eventstream activity. Your monitoring data will appear in the Workspace Monitoring database within minutes. Eventstream activity" within Monitoring. The panel highlights an active toggle switch, a description explaining that enabling this feature emits performance and error metrics to a monitoring database. To learn more, refer to the Monitor Eventstream data flows in Workspace Monitoring documentation. Eventstream UI editor improvements (Preview) We've redesigned key parts of the Eventstream editor to make building and troubleshooting faster and more intuitive. Always Publish: No more blocked publish buttons. Publish your work at any stage, the editor gives you clear, contextual guidance on what still needs attention instead of preventing you from moving forward. Inline error indicators: Errors now appear directly on the node that needs fixing, with actionable guidance on click. No more hunting through a detached error list to find what's broken. Operator and destination descriptions: Each option now includes an inline description explaining what it does, so you can build confidently without switching to docs. These changes reduce friction during authoring and make it easier to go from idea to running your pipelines. Secure Azure Event Hubs Connections in Eventstream with Workspace Identity (Preview) Bringing real time event data into Microsoft Fabric is now simpler and more secure with Azure Event Hubs integration for Eventstream. Organizations can connect Event Hubs directly to Eventstream and start routing event data to destinations such as Eventhouse for analytics and operational insights. A key capability is Workspace Identity, which removes the need to manage shared access keys. Instead, Eventstream can authenticate to Azure Event Hubs using the Fabric workspace identity. Administrators simply grant the workspace the Azure Event Hubs Data Receiver role, enabling secure access through Microsoft Entra based permissions. This approach improves security, simplifies credential management, and aligns with enterprise governance requirements. The integration supports both public and private network deployments. For Event Hubs hosted in private networks, organizations can connect through a streaming virtual network gateway while continuing to use Workspace Identity for authentication. For advanced event processing scenarios, users can enable schema support, associate schemas from the event schema registry, and route structured events to destinations such as Eventhouse. Combined with Workspace Identity, this provides a secure and scalable foundation for building real time data pipelines without the operational overhead of managing secrets or credentials. Schema Registry and Event SchemaSet Region Availability Previously, Schema Registry feature and the Event SchemaSet artifact was available for preview in 31 regions. Expanded to 10 additional regions: Geography Region Americas Central US Americas Mexico Central Americas West US 3 Europe Italy North Europe Poland Central Europe Spain Central Europe West Europe Asia Pacific Australia Southeast Asia Pacific Israel Central Asia Pacific Japan West If you were previously blocked from trying SchemaSets and schema-based Eventstream data ingestion, you can now do so in these regions. For more information on Event SchemaSets and how you can create and manage them, visit Schema Registry Overview. To learn more about configuring Eventstreams with schema-based sources, visit Use schemas in Eventstreams. Data-driven styling and UX improvements for Maps (Generally Available) Maps become most valuable when they help users understand not just where things are, but what the data means. Now, Data-Driven Styling, along with Markers Rotate by Data, Traffic Flow visualization, additional Map View options, and an improved Layer Settings experience. Together, these enhancements help organizations transform raw geospatial data into intuitive, actionable business insights. Let Your Data Tell the Story Understanding patterns hidden within geographic data can be challenging when every feature on a map looks the same. With Data-Driven Styling, Fabric Maps enables map builders to visually represent business data directly on the map, helping viewers quickly identify trends, hotspots, and outliers without inspecting individual records. The new Color by Value Range capability allows authors to style map layers using numeric measures such as revenue, utilization, sensor readings, environmental measurements, or operational KPIs. Instead of applying a single color to an entire layer, Fabric Maps visualizes value distributions chromatically, making important differences immediately visible. Organizations can choose between two visualization approaches: Gradient Styling uses continuous color transitions to reveal magnitude, trends, and geographic variation across a dataset. Step-Based Styling allows users to define custom value ranges with distinct colors, making it easy to visualize business thresholds, risk levels, performance bands, or service categories. To make these visualizations easier to interpret, Fabric Maps automatically generates corresponding data legends that explain how colors map to underlying values. This helps viewers understand the meaning behind the visualization and make decisions with greater confidence. Fabric Maps also includes thoughtfully designed color palettes, including options that support colorblind-friendly visualization scenarios, helping more users accurately interpret map-based insights. Combined with clear, automatically generated legends, these capabilities improve accessibility and make data-driven maps easier to understand across a wider range of audiences. per street and road and high-value exposure in flood-prone areas. Visualize Direction and Movement with Markers Rotate by Data Many operational scenarios involve not only location but also direction. Fabric Maps now supports Markers Rotate by Data, allowing marker symbols to automatically rotate based on values stored in a data column. This capability is available for Marker layers, enabling builders to visualize directional information directly on the map. Whether visualizing aircraft headings, vehicle movements, equipment orientation, or other operational workflows, map authors can represent direction without requiring custom visualization development. By transforming static points into directional indicators, organizations can add valuable operational context and communicate movement patterns more effectively. Add Real-World Context with Traffic Flow Visualization Location data alone doesn't always tell the full story. Fabric Maps now supports Traffic Flow overlays, allowing map builders to bring current traffic conditions into their existing map experiences. By combining business data with real-world traffic information, organizations can gain additional situational awareness for logistics operations, field service planning, transportation monitoring, and operational decision-making. The added context helps users better understand the environment surrounding their assets and activities without leaving the map experience. Configure the Right Map View for Your Audience Organizations often create maps for users across different regions and business contexts. Fabric Maps introduces additional Map View settings that allow map builders to configure how geographic information is presented, helping ensure maps are displayed in a way that aligns with organizational needs and audience expectations. This flexibility gives authors greater control over creating a consistent and intuitive viewing experience across a variety of business scenarios. A More Discoverable Authoring Experience UX research and user feedback showed that some key layer settings were difficult to discover. We updated the experience by moving geometry and visualization options—including visual type, Data-Driven Styling, and marker rotation—higher in the configuration pane. We also renamed General to Visibility, making the settings clearer and map authoring more intuitive. Turn Location Data into Business Insight Data-Driven Styling, built-in data legends, Markers Rotate by Data, Traffic Flow overlays, enhanced Map View options, and the improved Layer Settings experience help organizations transform location data into meaningful business insight. Together, these capabilities make it easier to uncover patterns, understand operational context, and communicate geospatial insights across teams. Start building with these capabilities today: explore the customization options, apply them to your own geospatial data, and create map experiences that turn location into action. To learn more, refer to the Customize a map in Microsoft Fabric documentation. Workspace Outbound Access Protection (OAP) for Operations Agent (Preview) Workspace Outbound Access Protection (OAP) in Microsoft Fabric helps admins secure outbound connections from workspace items to external resources. Admins can control outbound access by blocking unwanted connections by default and allowing only approved connections through configured rules. As organizations adopt AI-powered operations at scale, governance and security remain critical requirements. With this preview release, Microsoft Fabric introduces Outbound Access Protection (OAP) for Operations Agent, enabling workspace administrators to control the outbound actions an agent can perform. OAP applies workspace-level policies to actions such as Teams notifications, workflow triggers, and cross-workspace operations, helping organizations enforce security and compliance requirements while continuing to benefit from AI-driven automation. When OAP is enabled, Operations Agent continues to perform core functions including reasoning, recommendation generation, rule evaluation, and telemetry collection. However, outbound actions are governed by the workspace's configured access policies. Administrators gain greater visibility through in-product notifications, Teams messaging experiences, and the Operations Agent Activity Log, making it easier to identify and troubleshoot blocked actions What's new with Operations Agent and OAP? Govern outbound agent actions through workspace-level OAP policies. Control whether Operations Agent can send Teams notifications based on allowed connections. Prevent unauthorized cross-workspace actions when OAP policies restrict outbound access. Receive clear visibility when actions are blocked through in-product notifications and Teams messaging experiences. Monitor agent activity and OAP-related outcomes through the Operations Agent Activity Log. During the preview, some limitations apply. Cross-workspace actions are blocked when OAP is enabled. For example, Power Automate actions are not yet supported when OAP is applied, and only connectors that explicitly support OAP policies can be permitted. By extending Fabric's outbound governance framework to Operations Agent, organizations can adopt AI-powered operational automation with greater confidence while maintaining control over how and where agent-initiated actions are executed. Resources Workspace outbound access protection for operations agent (preview) Workspace Outbound Access Protection (OAP) Workspace Outbound Access Protection for Operations Agents Configure and manage Activator rules directly in Eventstream (Generally Available) You can now create and manage rules directly in Eventstream. Previously, setting up an alert required switching from Eventstream to Activator. While powerful, this meant moving between experiences to complete a single workflow. Now, alert creation is embedded directly into Eventstream. Capabilities for building or editing your Eventstream: Select the stream you want to monitor. Choose Set Alert. Define your condition (thresholds, aggregations, patterns). Configure the action. Create the rule. Capabilities for Activator destination created on Eventstream: Select Activator node Select Rule icon Create the rule Once you have the rule(s) created on your Eventstream, you can manage them by editing, deleting or opening in Activator. To learn more, refer to the Add a Fabric activator destination to an eventstream documentation. Data Factory Introducing Hierarchical Navigation in Monitoring Hub for Fabric Pipelines Modern data estates rarely consist of a single job running in isolation. Pipelines trigger notebooks, notebooks invoke other workloads, and business processes span multiple interconnected executions. When troubleshooting a failure or understanding lineage, customers often need visibility into how these executions relate to one another. Hierarchical Navigation in Monitoring Hub, is a new capability that helps you understand the relationships between runs and quickly navigate across upstream and downstream executions. With Hierarchical Navigation enabled, Monitoring Hub can display: Upstream runs that initiated a workload Downstream runs triggered by a workload Execution relationships across supported Fabric artifacts This provides a richer observability experience by helping you move beyond individual run monitoring and understand how your workloads operate together. This enhancement is another step toward a richer observability experience in Fabric, helping customers gain deeper insight into workload execution and dependencies at scale. To learn more, refer to the Hierarchical Navigation for Pipelines in Monitoring Hub documentation. Explore the modern Fabric Pipeline canvas (Preview) The new Fabric Pipeline canvas experience is designed to make pipeline authoring easier than ever. Key capabilities with modern canvas: Better visibility when navigating large pipeline graphs Cleaner, more structured layouts for complex orchestration logic Improved responsiveness when working with enterprise-scale workflows A more intuitive experience for pipelines with many activities and branches The modern Fabric Pipeline canvas, showing the updated node experience and option to disable the preview if needed. If you haven't tried it yet, now's the perfect time. The new experience is rolled out automatically and can be disabled at any time. Whether you're building your first pipeline or managing hundreds of activities across complex workflows, the new canvas is designed to help you stay productive and focused on what matters most. To learn more, refer to the Modern Pipeline Node Experience documentation. Upgrade Dataflow Gen1 to Dataflow Gen2 (CI/CD) using the Upgrade Wizard (Preview) The Dataflows Upgrade Wizard is a guided, end-to-end experience that upgrades your existing Power BI Dataflow Gen1 items to Dataflow Gen2 (CI/CD) in Microsoft Fabric with minimal effort. You can upgrade a single dataflow, or several dataflows from a workspace in a single flow. Previously, bringing a Gen1 dataflow into Fabric meant recreating it and repointing everything that depended on it. The Upgrade Wizard upgrades in place instead. Each dataflow keeps its ID, name, schedule, and connections, so the reports and semantic models that connect to it keep working without any changes. Before anything changes, the wizard assesses every dataflow in the workspace and tells you which ones need attention and why, such as incremental refresh settings to reconfigure or a linked entity to update, so you know what to expect before you upgrade. Why this matters Upgrade in place, with nothing to rebuild and nothing to repoint. Upgrade a whole workspace at once instead of one dataflow at a time. See what needs attention before you upgrade. Unlock Dataflow Gen2 innovations: improved performance, deeper Fabric integration, CI/CD and Git support, data destinations of your choice, richer diagnostics, Copilot-assisted authoring, and a modern data transformation foundation. The wizard is available for Dataflow Gen1 items in Premium or Fabric workspaces and requires Fabric to be enabled. To learn more, refer to the Upgrade Dataflow Gen1 to Dataflow Gen2 (CI/CD) using the Upgrade Wizard documentation. Extended watermark support in Copy job Watermark-based incremental load support enables Copy Job to efficiently ingest only new or changed data from key enterprise and SaaS data sources, avoiding costly full reloads. This reduces source-system impact, network traffic, and runtime while improving scalability for production analytics workloads. Customers can now use incremental loading across more of their critical data sources, including Salesforce, Informix, Cassandra, Greenplum, Presto, and Databricks, by leveraging Copy job’s built-in watermark mechanism and without building custom ingestion logic. To learn more, refer to the Incremental copy in Copy job documentation. Enable Change data feed during Lakehouse table creation in Copy job Copy job can now create Lakehouse tables with Change Data Feed (CDF) enabled automatically. There’s no longer a need to pre-create destination tables or manually configure Delta table properties. Simply select Enable CDF on destination and Copy job takes care of the setup for you. This ensures the table is immediately ready for incremental processing and downstream CDC scenarios, helping reduce data movement and improve replication efficiency. By eliminating manual configuration steps, it makes advanced data integration patterns much easier to adopt and operate at scale. To learn more, refer to the Automatic table creation and truncation on destination documentation. Amazon Redshift as new source in Copy job As part of our mission to enable multi-cloud data movement at petabyte scale with Copy job, we are bringing Amazon Redshift support as a source. This enables customers to seamlessly ingest data from one of AWS's most widely adopted data warehouse platforms directly into Fabric. Redshift support further strengthens Fabric's vision of delivering an open, connected, and multi-cloud data platform. To learn more, refer to the Connectors for Copy Job documentation. Copy job supports timestamps without time zone in Lakehouse Support for timestamps without time zones (timestamp_ntz) allows Fabric Lakehouse tables to preserve date and time values exactly as stored, without applying time zone conversions. Copy job can now automatically map timezone-independent datetime values to Delta Lake timestamp_ntz, ensuring greater compatibility with source systems that allow storing date and time values without time zone information. Migration Assistant for SQL database in Fabric (Generally Available) The guided, Fabric-native wizard takes you from a source SQL Server schema to a running SQL database in Fabric: upload a DACPAC, review compatibility results, deploy the schema with Copilot-assisted fix suggestions, and copy your data using built-in Fabric Copy Jobs. We've added the capability preview customers asked for most: Validate. You can now check out a DACPAC for compatibility before creating a SQL database in Fabric. Upload the file and the assistant reports which schema objects will deploy cleanly, which ones use features that aren't supported, the reason behind each failure, and the dependencies between objects — with nothing provisioned and no capacity consumed. That means you can scope migration effort, plan remediation, and get change-approval sign-off before you commit to a target database. To get started, select Migrate in your Fabric workspace and choose Migrate to SQL database in Fabric. To learn more, refer to the Fabric Migration Assistant documentation. Until next month That's a wrap for the August 2026 Microsoft Fabric Monthly Update. As always, we'll continue sharing new capabilities, enhancements, and improvements across Microsoft Fabric in future monthly updates. Thank you for being part of the Fabric community!
Krakowskie Spotkanie Grupy Power BI
Agenda Intro Łukasz Balcerzak - "Grupy obliczeniowe (calculation groups) w praktyce - wprowadzenie i przykłady zastosowania" (300) Pizza break/Networking Mateusz Motoła - "Power BI Overview" (100) Outro (i dojadanie pizzy ) Bio Łukasz Balcerzak Pracuję jako analityk biznesowy w środowisku Microsoft. Na co dzień zajmuję się modelami tabelarycznymi Power BI wykorzystujac narzędzia Tabular Editor, DAX Studio czy SQL Server Management Studio. Przygodę z danymi zacząłem w 2018 od Excela i SPSSa, pomału rozwijając kolejne kompetencje. W analizie danych największą satysfakcję sprawia mi wyciąganie informacji że zbiorów danych i prezentowanie ich na intuicyjnych wizualizacjach. Mateusz Motoła Jestem fascynatem dziedziny Business Intelligence z ponad dziesięcioletnim doświadczeniem w środowisku korporacyjnym. Obecnie pracuje jako Senior BI Developer w Coca-Cola Hellenic gdzie na co dzień wykorzystuję platformę danych Microsoft budując narzędzia analityczne. Dzięki pasji do ciągłego uczenia się z przyjemnością odkrywam granice możliwości ulubionego narzędzia jakim jest Power BI. W projektach cenię nieszablonowe myślenie oparte o szeroką perpspektywę, które pozwala na efektywne rozwiązywanie problemów.DAXdakan Ep3: Dashboard Design Workflows
This is the 1st week we asked you to determine the DAXdakan topic... Based on the survey, let's discuss real-life workflows during dashboard design! DAXdakan is a fortnightly (once every 2 weeks) Teams meeting with other Power BI-erists (Power BI Pilipinas members). Language is Filipino/English. Attendance is free.DAXdakan 1:many w/ PBI Pinas Moderators
This is it, DAXdakan with your PBI Pinas Community moderators! Hear their data journeys, ask them anything! Join us for another very special edition of DAXdakan where we meet: • Drizzle Pablo (TechBayanihan, Bizapps Pinoy Community) • Alex Gamboa (Chad GPT) • Dane Belarmino (DAXJutsu) • Arman Castroverde • Rico Balibalita • Dennis Demandaco • Jemar Lim • Adam Christian Amistad DAXdakan is a re-occuring Teams meeting with other Power BI Pilipinas members. Language is Filipino/English. Attendance is free. Join us online via Teams. Watch us live via Facebook Live. Or wait for recorded versions to be uploaded in Youtube, Spotify, and our other social media channels. While you're accepting the invite, please join our official Microsoft Community: https://community.fabric.microsoft.com/t5/Power-BI-Pilipinas/gh-p/pbi_philippines_usergroup?WT.mc_id=DP-MVP-5005302DP-800 & Microsoft Fabric
🚀 Live Community Session: DP-800 & Microsoft Fabric How does the new DP-800: SQL AI Developer Associate certification fit into the Microsoft Fabric ecosystem? Join us for a live community session with Kevin Chant as we explore the connection between DP-800 and Microsoft Fabric, what the certification covers, where Fabric fits into the picture, and what it means for SQL professionals and developers working with Microsoft's modern data platform. 📅 Tuesday, July 21 🕕 18:00 UTC 🎤 Speaker: Kevin Chant 📍 Microsoft Fabric Discord Server Whether you're curious about the new certification, working with Fabric today, or interested in where AI-powered SQL development is heading, we'd love to have you join the discussion. See you there! 🚀A new analytics frontier: GPU-accelerated Fabric Data Warehouse (Early Access Preview)
As data volumes grow, concurrency rises, and analytics workloads become more dynamic and AI-driven, performance becomes harder to predict and harder to scale. Every query sits in the critical path, adding pressure to the warehouse, and every second counts. This is the core tension in analytics today. The expectations have changed, but the underlying technology has not, leaving agents, applications, and AI systems waiting on data. To meet this moment, analytics needs a new kind of execution engine.
Microsoft Fabric September 2023 Update
Welcome to the September 2023 update. We have lots of features this month including updates to the monitoring hub, Fabric Metrics app, VS code integration for Data Engineering, Real-time data sharing and many more. Continue reading for more details on our new features! Contents Core Monitoring hub – column options OneLake OneLake file explorer (v.1.0.10) - Menu Option to View Workspaces and Items Online Power BI General Upgrade Power BI Desktop .NET Framework to 4.7.2 or newer Reporting Mobile layout interactive canvas Smart Narrative improvements Modeling Edit your data model in the Power BI Service - Updates Edit linguistic relationships in Q&A setup DAX function changes to MINX and MAXX Edit relationships in the properties pane (GA) Data Connectivity SAP HANA (Connector Update) Emplifi Metrics (New Connector) Service Row-level security test as role improvements Mobile Supporting AAD Shared device mode (preview) Developers Git integration Visualizations New visuals in AppSource Change Chart Beeswarm Chart Explore time-based data down to each millisecond with Drill Down TimeSeries PRO New Updates for accoPLANNING (Release 57) Zebra BI Charts on-visual settings Word Cloud by Powerviz Innofalls Charts: A Versatile and Interactive Visual Hierarchy Chart by MAQ Software Power BI JSON Report Theme Generator by BIBB Synapse Data Warehouse Data Warehouse Utilization Reporting in Fabric Metrics app Column-Level security is now available on Data Warehouse and SQL Endpoint Data Warehouse Row Level Security SQL Projects support for Fabric Data Warehouse Deployment Pipelines Default Dataset Improvements Data Engineering VS Code Integration Introducing Notebook File System support in Synapse VS Code extension for Data Engineering and Data Science workload in Microsoft Fabric Import Notebook in Workspace view Mssparkutils new API for fast data copy Support diff versions when saving notebook Notebook Sharing with Execute-only mode Notebook resources .whl file support Real-time Analytics OneLake shortcut to delta tables from KQL DB Real-Time Data Sharing: Introducing database shortcuts in Real-Time Analytics Sample Gallery – Explore what you can achieve in Real-Time analytics Model and Query data as graphs using Kusto Query Language (KQL) Easily connect to KQL Database from Power BI desktop Eventstream now supports AMQP format connection string for data ingestion Eventstream supports data ingestion from Azure IoT Hub Improved Eventstream Creation Speed Data Factory Data Pipeline Dataflow Gen2 New Service Principal authentication kind Bug fixes and reliability improvements Community New learning path: Implement a Lakehouse with Microsoft Fabric Core Monitoring hub – column options We have released a new feature inside the monitoring hub to better users customize their experience. Column options gives users more room to operate. Users can select and reorder the columns that meet the scene according to their customized needs. Regardless of whether the user switches to any other tool or scene in Fabric, these columns options are persisted along with the filter until the user returns again. You can now easily reorder the columns with drag and drop inside the option menu. You do not need to worry about the ones at the bottom. All the selected column options will be bubbled up to the top of the menu when you open it next time. Try it out yourself! OneLake OneLake file explorer (v.1.0.10) - Menu Option to View Workspaces and Items Online We are excited to announce the new release of OneLake file explorer for Windows! Now you can seamlessly transition between using the OneLake file explorer app and the Fabric web portal. When browsing data in OneLake file explorer, right click on a workspace and select “OneLake->View Workspace Online.” Selecting this will open the workspace browser on the Fabric web portal. Similarly for items within your workspace. In addition, you can now easily find your client-side logs, which you may need to troubleshoot issues. Right-click on the OneLake icon in the Windows notification area, select Diagnostic Operations, then Open logs directory. Get started by downloading the latest OneLake file explorer. Power BI General Upgrade Power BI Desktop .NET Framework to 4.7.2 or newer Power BI Desktop with .NET Framework 4.5 will no longer be supported after 9/30/2023. Please upgrade to .NET Framework 4.7.2 or newer to avoid any support issues. Reporting Mobile layout interactive canvas With this latest update we are happy to announce that we have made the mobile canvas interactive. This new capability provides the ability to test how buttons, slicers, and visuals will behavior on the app before publishing the report. But that's not all. With this canvas interactivity, users can now interact with visuals directly and adjust Table and Matrix column headers to align perfectly with mobile screens. Smart Narrative improvements The Smart Narrative visual can now display either text or numeric values when showing data for a column. Previously this was limited to only numeric values. Learn more about the Smart Narrative’s summarization capabilities at Create smart narrative summaries - Power BI | Microsoft Learn. Modeling Edit your data model in the Power BI Service - Updates The new data model editing in the Service feature was released to preview in April. We’ve been busy reacting to your feedback and enhancing the experience. Below are the improvements coming later this month: Improve layouts limitations Changes you make to layouts will now persist between Desktop and the Service, including: Changes made to data model layouts in Desktop will now be incorporated into the Service upon uploading the .PBIX file. Similarly, changes made to layouts in the data model within the Service will now be incorporated in the Desktop upon downloading the .PBIX file. Please continue to submit your feedback directly in the comments of this blog post or in our feedback forum. Edit linguistic relationships in Q&A setup The Q&A visual is an effective way to help users further understand their data by asking questions and receiving answers in the form of visuals. It offers users a way to explore their data in ways not covered by the rest of the report without requiring deeper knowledge of their data model or report authoring. However, while the Q&A engine is good at answering precise questions about data, it may not be able to associate every word or phrase a user inputs with data in the model. For example, answering “what are our best consoles this year?” may require connecting the term consoles to the name products in the model, and understanding that the term best corresponds to the highest sales values. These terms are contextual, however – users could mean something completely different asking for console and best in other industries, organizations, or even datasets. To help authors ensure that the Q&A visual provides consistent and accurate answers based on the unique language their report consumers actually use, we introduced Q&A setup tools with an emphasis on providing Q&A with synonyms for column and table names in the model. This way, authors can explicitly define console as referring to products, and users will always receive the correct answers when they ask similar questions in the future. However, synonyms (nouns) are only half of the picture. The other half of the terms (adjectives, verbs, prepositions, adverbs) can’t be defined with such straightforward mappings because they must be understood as a part of a phrase – they qualify other terms or relate other terms together. Best in the previous example is one; asking “who sold the most books” requires us to know that stores sell books, connecting stores to books. There are many types of these linguistic relationships, so we built a new tab entirely to help you create and manage linguistic relationships for your data. You can get into the Q&A setup menu using the gear icon on the Q&A visual or the Q&A setup option in the Modeling tab of the ribbon, then selecting the new Relationships tab. There, you’ll be able to define a variety of relationships, including verb, adjective, noun, preposition, and more. Choose a type which fits the term you’re trying to define (for example, “best” is an adjective), then follow the prompts to define what it means in the context of your data. Our investment into Q&A does not stop here. Even in a world where natural language capabilities are increasingly driven by large language models, there is value in the precision, consistency, and customizability of our sophisticated Q&A engine. In the other direction, defining synonyms and relationships can be a lengthy process just asking to be streamlined with the power of AI-generated suggestions. Keep an eye out in the future for the ways we’re bridging the two to bring out the best of both worlds! DAX function changes to MINX and MAXX We have added an optional variant parameter to MINX and MAXX DAX functions. These functions ignore text and Boolean values when there are variants or mixed data types, such as text and numeric. Now with the new optional variant parameter set to TRUE, the functions will consider the text values. Here is an example of a variant measure. In the table below the variant measure is ordered in ascending order: MINX and MAXX without the variant parameter set, or set to FALSE, will ignore text and Boolean data types. MINX and MAXX with the variant parameter set to TRUE will now include text values. Boolean values are still ignored. The default of the MINX and MAXX optional variant parameter is FALSE, so all existing DAX expressions using MINX and MAXX will not be impacted by this change. To allow text values to be included, you can update the DAX expression to include TRUE as the third parameter. Read more about these changes at Microsoft Learn: MINX function (DAX) - DAX | Microsoft Learn MAXX function (DAX) - DAX | Microsoft Learn Edit relationships in the properties pane (GA) We are excited to announce the general availability of the edit relationships in the properties pane! The edit relationships in the properties pane has been available for public preview since the October 2022 release. Now you can click on any relationship line in the modeling to edit your relationship in the properties pane. This is available in the Model view of Power BI Desktop and in web modeling of the Power BI service. Edit relationships without running queries to preview the data and only validating the relationship when you click apply changes. A welcome relief for those using DirectQuery storage mode, as these queries can take time and impact your data sources. You can learn more about how to utilize this feature at Create and manage relationships in Power BI Desktop - Power BI | Microsoft Learn. Data Connectivity SAP HANA (Connector Update) The update enhances the SAP HANA connector with the capability to consume HANA Calculation Views deployed in SAP Datasphere by taking into account SAP Datasphere’s additional security concepts. This enables consumption of Calculation Views in Datasphere and allows customers to connect to HANA Cloud views without the need for additional privileges on the _SYS_BI schema. Emplifi Metrics (New Connector) We are happy to announce the release of the new Emplifi Metrics connector. Please find release notes from the Emplifi team below: “Integrating social media insights alongside the rest of your marketing or business intelligence data gives you a holistic understanding of your entire digital strategy, all in one place. With Emplifi Power BI Connector, you’ll be able to include social media data from the Emplifi Platform in your charts and graphs and combine them with other data you own. The Power BI Connector is a layer between Emplifi Public API and Power BI itself. It helps you work with your data intuitively, directly in the Power BI tool. The majority of data and metrics available in the Emplifi Public API are also available in the Connector. Please visit the official documentation for more information about Emplifi Public API and a list of available metrics. You’ll find it here: https://api.emplifi.io/.” Service Row-level security test as role improvements We have listened to your feedback about the row-level security test as role experience in the Service and have made several improvements this month including: We have added a new dropdown, allowing you to choose and test any report connected to the dataset. You can now easily see important permissions details pertaining to a specific individual while role testing for that person. We have improved our warning and error messages. To learn more about validating row-level security roles in the Service read our documentation. Mobile Supporting AAD Shared device mode (preview) We, in Power BI continue our investments in empowering frontline workers with data and insights in their work. And in this month release, the Power BI mobile apps add support in Azure active directory shared device mode. Shared Device Mode enables you to configure devices to be shared by a group of employees. This is very common for frontline workers, who do not use their personal device for work related tasks but are getting a work-related mobile device from a shared pool of devices for these tasks. The Shared Device Mode enables single sign-on (SSO) and device-wide sign out for Microsoft Power BI and all other apps that support Shared Device Mode. Once a user signs in into a supported app, the user will be logged into any application compatible with Shared Device Mode as well. Once a user signs out of a supported app, all other applications integrated with shared device mode sign out, to to prevent unauthorized or unintended access by the next user. Back to the frontline worker scenario: when an employee picks a device from a pool at the start of their shift, they need only a single sign-in to one supported app (for example Power BI), and they’re automatically signed in to all other apps that support shared device mode on the device, so the device and the apps are ready for work with the user’s account. At the end of their shift, when they sign out of one app, they're signed out globally from all other apps that support shared device mode, having the device is ready for the next employee and can be safely handed off. Developers Git integration Paginated reports now supported with git integration Since Git integration has been launched few months ago, we supported only 2 items in Power BI- Power BI reports and Power BI datasets. We are now excited to add a new item- Paginated reports! After connecting your workspace to Azure DevOps, you can commit your paginated reports and have them versioned in your repository. After that, you can open the .rdl files directly from git in Power BI Report Builder, edit and push the changes into git. The workspace will identify the changes and will prompt developers to update the workspace with the changes to the paginated report. With this feature, we are adding paginated reports developers to enjoy the collaboration, versioning and modern developer workflows offered in Power BI and Fabric. Learn more about using git with paginated reports. Checkout and Switch Branches in a Workspace A workspace can connect to a single branch at a time. Once you’ve setup your separate workspace to work with git, there might be scenarios where you want to change just the connection of a branch. We have now added 2 new features to help you achieve this much faster: Checkout branch- checkout allows you to easily create a new branch, based on the current state of the workspace. After choosing to checkout, you can create a new branch that will be connected to the WS, while the uncommitted changes are retained. This is useful in cases of conflicts, that allows you to commit your changes to a backup branch, and then manage the merge conflict in the git repo. Switch branch- Workspace admins can decide to change just the connection of the branch very easily. It’s helpful in cases you want to start working on a new branch and wipe clean your WS content for that, or when you are moving from your regular project to small bug fixes (assuming they are on the same content), that should happen on a different branch. New E2E CI/CD tutorial now available Many users have been asking, since the release of git integration, how to work with deployment pipelines and git as part of a CI/CD process. These two features can help build a compelling e2e flow, when connected properly. Follow this step-by-step guide to learn how to make the most out of both tools together. Visualizations New visuals in AppSource Change Chart Pro Circle Card by Devlup Funnels Apex Milestone Trend Dot Chart Activity Gauge by Powerviz Bridger Visual by BI Samurai Number lines by BI-Champ Funnel Chart by Powerviz Date Picker by Powerviz Bar Chart with Export Data Feature Wordcloud by Powerviz PackedBubbleChart Box Ploty by Devlup Funnels LeapLytics - Markdown Viewer Aimplan Data Input Table Beeswarm Chart Overview of Beeswarm Chart Beeswarm chart shows the distribution of data along one axis or both axes while also showing individual points. It is like a one-dimensional or two-dimensional scatter plot but with closely-packed, non-overlapping points. How to use this Visual The visual is intuitive and easy to follow. Only 1 field is mandatory i.e. Bee Category. Tip: If you don’t have any category, you can simply add a calculated column with any name and use it in the category field Bee Size field is optional and is represented in the visual by bubble area or size. If this field is missing then all bubbles will be of same size Bee Label field is also optional. It is used to add labels to each individual bubbles. This chart can be used in 3 ways Y – Axis only X – Axis only Scatter Plot (Both Y and X Axes) 1) Y-Axis Only As shown in picture below, if you only use a column/field in “Beeswarm Y-Axis” section while keeping “Beeswarm X-Axis” section empty, your data will be plotted on Y-Axis categorized according to the “Bee Category” section 2) X-Axis Only Similarly, if you only use a column/field in “Beeswarm X-Axis” section while keeping “Beeswarm Y-Axis” section empty, your data will be plotted on X-Axis categorized according to the “Bee Category” section 3) Scatter Plot (Both X and Y Axes) If you use columns/fields in both sections i.e “Beeswarm X-Axis” section and “Beeswarm Y-Axis” section, the visual will work like a scatter plot with data points plotted on X-Axis and Y-axis . This is depicted below You can show images inside bubbles as well using the “Images” field. For example, the report below shows top millionaires of the world with their net worth on Y-Axis and their Age on X-Axis. Its available in demo file. Formatting Options These options are available in the format pane of the visual under “Settings”. 1) Bubble size As the name implies, this setting simple increases / decrease the bubble size 2) Lower Upper and Left Space Beeswarm chart produces non colliding bubbles or in other words circles don’t overlap. This can often cause bubbles do go outside the chart area especially when there is a concentration of data around minimum and maximum data values. This is where these settings are very useful to bring data inside the chart area. These settings increase or decrease the Y-Axis and X-Axis range to achieve this objective. Try adjusting these settings on the demo file’s sheet “ScatterPlot Beeswarm” to see how they work. Purchase of Premium Features To use the visual without Watermark in Power BI Online, please purchase the Visual’s license for a 1 year period. The license is for unlimited number of users and viewers. If you have any questions, please send me email at [email protected] Download Download the demo file here. Download the custom visual from APPSOURCE Explore time-based data down to each millisecond with Drill Down TimeSeries PRO Drill Down TimeSeries PRO lets you create timeseries charts that are easy to explore on any device. Using its intuitive interactions, users can drill down to months, days or hours by clicking on the chart. Combine up to 12 series and choose between multiple charts - line, column, area. Learn more about Drill Down TimeSeries PRO! MAIN FEATURES: On-chart interactions – pan, zoom and drill down Full customization – set intervals, min/max values, colors, fonts, stacking and clustering Static and dynamic thresholds – set up to 4 thresholds to demonstrate KPIs Cross-chart filtering – select data points on multiple charts instead of using slicers Touch device friendly – explore your data anywhere POPULAR USE CASES: Finance – stock exchange indices, capital ratios, transaction volumes Sales – web traffic, audience reach, lead volume IT – network traffic, response times, syslog and error trends Logistics – inventory movement and turnover, loading time, fleet maintenance costs Get Drill Down TimeSeries PRO now! This visual comes with 30 days free access to paid features. ZoomCharts Drill Down Visuals are known for interactive drilldowns, smooth animations and rich customization options. They are mobile friendly and support: interactions, selections, custom and native tooltips, filtering, bookmarks, and context menu. New Updates for accoPLANNING (Release 57) accoPLANNING for Power BI empowers business users with writeback capabilities, advanced planning, forecasting, budgeting, project management and analysis solutions. We have some new and exciting features for you in this release: • Custom calculated rows and columns configuration. It allows you to customize your own client-side calculations in your already established accoPLANNING table. • End user Lock cells. This gives the flexibility for the end user to lock cells and make sure these will not be affected by splashing or use as an approval indicator. • Hide rows and columns. This Allows users to hide rows and columns - adding flexibility in making asymmetric column and row selections in the grid for better reporting or just for ad-hoc purpose to get a better overview. The latest update also includes a range of new features and improvements designed to increase your productivity: • Our navigation has undergone a major overhaul, with the most notable change being the revamped toolbar. • Better API error message handling in the grid. • Support for automatically expanding all rows/columns/both, eliminating the need to manually adjust the size of each cell. With the accoPLANNING visual, you combine the planning and reporting process in Power BI. For more information, visit our website. https://www.accotool.com/ https://appsource.microsoft.com/en-us/product/power-bi-visuals/WA200002600?tab=Overview https://youtu.be/kNIVC6rBJWA Zebra BI Charts on-visual settings Zebra BI visuals for Power BI are taking another step further to make the user experience as seamless and interactive as possible. Zebra BI Charts has received some important updates so that you save time while customizing your visuals. Thanks to the on-visual settings, you don’t need to go to the visualizations pane every time you want to make a change. COMMENTS SETTINGS can now be adjusted directly on the visual by simply clicking on the settings icon next to the comment box. You can adjust the title, which variances you display, the icon, gap between the comments, and padding. CATEGORY SETTINGS let you adjust several settings by just clicking on the category area. Customize the axis font (family and color), trim /rotate long labels, adjust the label density, and set the gap between the columns. LEGEND SETTINGS come in handy when you want to rename the entries and don’t want to search for this option in the formatting pane. Adjust margins, use aliases in tooltips and switch comparisons. STACKED CHARTS SETTINGS on the visual let you adjust the Top N feature, set color of the chart, and display labels as %. Try it on your data for free. Word Cloud by Powerviz We are excited to announce the new Word Cloud by Powerviz, it's an advanced visual which empowers you to create some of the most high-quality and creative word art in the Power BI. Key Features: Word Styling: Make your word clouds pop with personalized text styles. It offers font styling, direction & text editing features. Color Options: Choose from 30+ color palettes, including color-blind safe options. Shapes: Want to make a statement? Create captivating word clouds by choosing shapes from icons and images, or upload your own image. Exclude: No more hassles anymore! Easily remove unwanted words, symbols from the text to create a clean and focused word cloud. Ranking: Filter out Top/Bottom N Words. Conditional Formatting: Easily spot words with dynamic rules. Many other features included lasso/reverse lasso, grid view, show condition, and accessibility support. Business Use Cases: Marketing: Sentiment analysis & SEO keywords. Education: Brainstorming, Improve engagement. Market Research: Opinion poll, open-ended survey. Presentation: Capture attention & easy communication. Try Word Cloud Visual for FREE from AppSource Check out the visual features in demo file Step by Step instructions and documentation To learn more, visit Powerviz website. Introducing Word Cloud by Powerviz – A Powerful Power BI Custom Visual on YouTube Innofalls Charts: A Versatile and Interactive Visual Innofalls Charts is a powerful visual that offers a wide range of interactive features. Our bar and waterfalls charts come with a drill-down history, enabling orientation and facilitating comparisons. All charts can be stacked or grouped into columns. Waterfalls: Our special attention to waterfalls includes running totals and delta waterfalls, enabling effective comparisons of measures or members. The waterfalls are expandable, drillable, stackable, and offer intermediate sums. Drill Down History: Keep track of your drill downs with visual feedback. Change your drill downs retroactively and combine multiple drill downs for in-depth exploration. New Comparison Features: Effortlessly make comparisons while viewing your report and incorporate deviation charts with a single click. Interactivity: Enjoy various interactive features such as drill, expand, undo/redo, dynamic comparisons, and zoom for enhanced data exploration. IBCS Formatting: Our IBCS theme provides scenario formatting, equal scaling, highlights, deviations and more for consistent and standardized reports. Animations: Enhance data comprehension and reduce change blindness with animations. Promptly detect resorted or added data elements. Discover the full potential of Innofalls Charts and get started today. Innofalls Charts is free for Power BI Desktop! Get started today [ https://www.innofalls.com/blog-get-now ] Visit our website [ https://www.innofalls.com/ ] Hierarchy Chart by MAQ Software Struggling to visualize complex hierarchical relationships? Improve understanding and decision-making with the Hierarchy Chart by MAQ software. In today's data-driven environment, visualizing hierarchical relationships is key to business insights and decision-making. From sales structures to budget allocations, our Power BI-verified visual offers a customizable way to represent these structures easily, catering to various business needs. Figure 1: Sample visual showing a color-coded organization chart (with a tooltip). Key business uses: Organization/HR: Organize human resources by department and hierarchy. Operations: Illustrate manufacturing processes, breaking down components or ingredients hierarchically. Finance: Show budget allocations by division or project, with color-coded subcategories to highlight differences. Sales: Visualize sales structure, territory responsibilities, and targets with group field indicators showing performance status. IT: Visualize IT assets by category and availability with detail. Key features: Color-code cards using a legend. Adjust card (i.e. box) size, borders, and corners. Customize the appearance of the links connecting each card. Interact with cards to control levels and cross-filter visuals. Zoom and reposition the visual with ease. Try out the features of the Hierarchy Chart by MAQ Software today on the visual’s sample report. Learn more about our Power BI custom visuals on our website. Power BI JSON Report Theme Generator by BIBB https://powerbithemegenerator.bibb.pro/ BIBB recently released its take on the Power BI Theme Generator, aiming to streamline the theming process for users. This new tool offers a user-friendly interface, allowing for an easy selection of colours and generation of JSON themes. Within BIBB's generator, users can choose colours in various ways, from manual selection to trending combinations and even importing from images or external sources like Coolors. Synapse Data Warehouse Data Warehouse Utilization Reporting in Fabric Metrics app We are excited to announce that Compute utilization reporting for Data warehouse and SQL Endpoint are now available in Public Preview for Microsoft Fabric across all regions! In the capacity-based SaaS world of Microsoft Fabric, customers can purchase a Fabric SKU and get entitled to a set of Capacity Units (CU). Various workloads, including data warehouse, consume capacity units based on usage. Fabric metrics app provides visibility into capacity usage for all Fabric workloads, including data warehouse in one place. It is used by Capacity or SQL warehouse admins to identify CU usage trends across warehouse items within a capacity, monitor overload information, and understand the cost of running operations which leads to informed capacity sizing decisions. For more information, see the detailed blog Data Warehouse Utilization Reporting in Fabric Capacity Metrics App Column-Level security is now available on Data Warehouse and SQL Endpoint We are excited to announce that Column-Level security is now available on Data Warehouse and SQL Endpoint. Column-level security simplifies the design and coding of security in your application, allowing you to restrict column access to protect sensitive data. For example, ensuring that specific users can access only certain columns of a table pertinent to their department. The access restriction logic is located in the database tier rather than away from the data in another application tier. The database applies the access restrictions every time data access is attempted from any tier. This restriction makes your security more reliable and robust by reducing the surface area of your overall security system. In addition, column-level security also eliminates the need for introducing views to filter out columns for imposing access restrictions on the users. You can implement column-level security with the GRANT T-SQL statement. Only Azure Active Directory authentication is supported. Data Warehouse Row Level Security We are thrilled to announce that Row-Level security is now available in Fabric Warehouse and SQL Endpoint. Row-Level Security enables you to use group membership or execution context to control access to rows in a database table. Row-Level Security (RLS) simplifies the design and coding of security in your application. RLS helps you implement restrictions on data row access. For example, you can ensure that workers access only those data rows that are pertinent to their department. Another example is to restrict customers' data access to only the data relevant to their company. The access restriction logic is located in the database tier rather than away from the data in another application tier. The database system applies the access restrictions every time that data access is attempted from any tier. This makes your security system more reliable and robust by reducing the surface area of your security system. Implement RLS by using the CREATE SECURITY POLICY Transact-SQL statement, and predicates created as inline table-valued functions. SQL Projects support for Fabric Data Warehouse We are excited to announce the Microsoft Fabric Data Warehouse as a supported target platform in the SQL Database Projects extension available inside of Azure Data Studio! SQL Database Projects for Azure Data Studio provides a way to design, edit, and publish schemas for SQL databases from a source controlled project. For a complete development workflow, build and deploy your database projects in CI/CD pipelines, such as GitHub Actions or Azure DevOps. A SQL project is a local representation of SQL objects that comprise the schema for a single database, such as tables, stored procedures, or functions. Use the SQL Database Projects to extract and publish warehouse schemas directly from and to Fabric Data Warehouse. Other compatible databases include SQL Server, Azure SQL Database, Azure SQL Managed Instance, and Azure Synapse SQL (serverless and dedicated). Get started today with the insiders build of ADS. Deployment Pipelines In today’s world, analytics is a vital part of decision making in almost every organization. Fabric's deployment pipelines tool provides BI creators with a production environment where they can collaborate to manage the lifecycle of organizational content. Deployment pipelines enable creators to develop and test content in the service before it reaches the users. Supported content types include reports, paginated reports, dashboards, datasets, dataflows, and now warehouses! The deployment process lets you clone content from one stage in the deployment pipeline to another, typically from development to test, and from test to production. The connections between the copied items are kept during the copy process as well. In addition, Fabric applies the configured deployment rules to the updated content in the target stage. You can also deploy content programmatically, using the deployment pipelines REST APIs. You can learn more about this process in Automate your deployment pipeline using APIs and DevOps. Default Dataset Improvements We have released some new capabilities to enhance the default dataset experience: Turn off automatically adding new objects to the default dataset – navigate to the Warehouse artifact settings to toggle this on/off Updates to the manage default dataset dialog in the model view Filter by schema Filter by object (table/view) We’ve heard your feedback about the ability to turn off the default dataset. Thank you for the feedback, we’re planning on delivering this feature in the coming months! Data Engineering VS Code Integration Introducing Notebook File System support in Synapse VS Code extension for Data Engineering and Data Science workload in Microsoft Fabric The Synapse VS Code extension empowers users to develop their notebook artifacts directly within the VS Code environment. Once users download the .IPYNB file to their local desktop, they gain the flexibility to edit, debug, and execute their notebook code locally. Furthermore, they have the option to select the kernel provided by the extension to execute and debug their notebook code on the remote Fabric Spark compute. Python developers commonly structure reusable functions as modules in the form of .py files, a well-established software engineering best practice. Now, with the incorporation of the Notebook File System within the VS Code Synapse extension, developers can apply this principle to their Fabric notebook development. In addition to running and debugging notebook code, developers can also execute and debug code from imported modules. This integration enhances code modularity and facilitates efficient development workflows. When a user clicks "Open Notebook Folder," the VS Code Synapse extension not only opens the selected .IPYNB file but also downloads all associated files, including .PY modules, from the notebook file system to the local VS Code environment. This feature ensures that the entire set of necessary files and modules is readily available for seamless development and collaboration within the VS Code interface. In this example, there is a .PY module named US2Europe.py which contains a function to covert the datetime format from US fashion to Europe’s one. To invoke this function from the notebook side and execute it on the worker node, you need to import the module containing the function and apply into the data frame To synchronize changes between the local environment and the remote workspace in the VS Code Synapse extension, users can take advantage of two key actions: Publish Resource Folder: Clicking the "Publish Resource Folder" button enables users to upload their local changes to the remote workspace. This ensures that any modifications made locally are reflected in the remote environment. Update Resource Folder: The "Update Resource Folder" button allows users to pull changes from the remote workspace into their local VS Code environment. This ensures that any updates made by collaborators or on the remote side are mirrored locally. In the event that a file has been deleted in the remote workspace but still exists in the local environment, the Synapse extension will automatically handle this situation as follows: The deleted file will be moved to a backup folder named "__backup" under the root directory of the building folder. This approach preserves a copy of the deleted file for reference, ensuring that no data is lost during the synchronization process. These actions provide users with an efficient and robust means to manage the synchronization of their files and code between local and remote environments while maintaining data integrity. Import Notebook in Workspace view We are excited to announce that we now have the “Import Notebook” entry on the Workspace -> New -> Import item! You can easily import one or more files (support .ipynb, .py, .sql, .scala, .r file format) as Fabric Notebook items in the target workspace. Mssparkutils new API for fast data copy We now support a new method in mssparkutils that can enable large volume of data move/copy much faster, which is: Mssparkutils.fs.fastcp() You can use mssparkutils.fs.help("fastcp") to check the detailed usage. According to our benchmark testing, we achieved ~7x to ~180x speed up (varies as different source type, data size and regions) compares to the traditional file system copy method mssparkutils.fs.cp(). As the below example shows, it takes ~5s to copy a 10G file from ADLS Gen2 account to Fabric Lakehouse. Support diff versions when saving notebook We now support viewing and comparing the differences between 2 versions of the same notebook when there are saving conflicts. This is particularly useful when more than one users are working on a same notebook instance via different clients, like: Publish notebook from Fabric VSCode extension Some users are using Manual save mode while others are collaborating Collaborating on notebook with unstable network With the diff function the users in collaboration session can get notified and compare the changes from other user’s edits, and they can choose a version to keep, or save a copy of any version, to easily handle the save conflicts without losing the code accidently. Notebook Sharing with Execute-only mode Fabric Notebook now supports checking the “Run” operation separately when sharing a notebook, if you just selected the “Run” operation, the recipient would see a “Execution-only” notebook after opening the item. With the “Execution-only” mode you can easily spread your notebook without worrying about someone messing up the contents, while the recipients can still run the cells. Notebook resources .whl file support We now support uploading the .whl files in the Notebook Resources explorer, you can put your own compiled libs here, use Drag & Drop code snippet to install them in the session and import the libraries in code conveniently. Real-time Analytics OneLake shortcut to delta tables from KQL DB Now you can create a shortcut from KQL DB to delta tables in OneLake, allowing in-place data queries. With this enhancement, you can effortlessly query delta tables in your Lakehouse or Warehouse directly from KQL DB. Learn more. Real-Time Data Sharing: Introducing database shortcuts in Real-Time Analytics A database shortcut in Real-Time Analytics is an embedded reference within a KQL database to a source database in Azure Data Explorer allowing in-place data sharing. The behaviour exhibited by the database shortcut is similar to that of a follower database. If you are a data provider hosting data in Azure Data Explorer and want to share this data with consumers in Microsoft Fabric, you can create a database shortcut. Database shortcut enables sharing in real-time, with no need to build or maintain a data pipeline. All database changes, including schema and data, on the provider side are instantly available on the consumer side. You can then consume data in Microsoft Fabric by using a KQL Queryset, PBI, Notebooks etc. easily . While today, you can share data from an Azure Data Explorer database with a KQL Database, soon you will also be able to share data from a KQL Database with other KQL Database(s) in Microsoft Fabric. Check out the announcement blog to learn more: Real-Time Data Sharing in Microsoft Fabric blog Sample Gallery – Explore what you can achieve in Real-Time analytics Real-Time analytics now offers a comprehensive sample gallery with multiple datasets allowing you to explore, learn and get started quickly. Selecting a sample in Real-Time Analytics automatically creates a KQL Database with data and prewritten KQL Queryset from any of the 6 sample datasets. This will let users to get started with KQL Database and KQL Queryset without the hassle of creating database, finding the right real time dataset, ingesting data, and writing queries. Each sample lets you explore different streaming and real time scenarios. The samples include data and queries for Stock analytics, Weather analytics, IOT analytics, Log analytics, Metrics analytics and Automotive operations analytics. Each dataset is accompanied by a KQL Queryset which includes sample queries introducing you to KQL capabilities such as. aggregations, search , transformations using update policies, user defined functions, geospatial and time-series analytics. Model and Query data as graphs using Kusto Query Language (KQL) We are excited to share with you a new public preview feature of Kusto Query Language (KQL) that allows you to model and query data as graphs. Graphs are useful for representing complex and dynamic data that involve many-to-many, hierarchical, or networked relationships, such as social networks, recommendation systems, connected assets, or knowledge graphs. By using KQL with graph semantics, you get the speed and scale of KQL queries with the flexibility and expressiveness of graphs. You can query graphs using KQL graph semantics, which has a simple and intuitive syntax that works well with the existing KQL features. You can also mix graph queries with other KQL features, such as time-based, location-based, and machine-learning queries, to do more advanced and powerful data analysis. For example, you can use time-aware graphs to examine how the graph evolves over time, or use geospatial queries to analyze the spatial distribution or proximity of nodes and edges. To learn more about graph semantics in KQL, check out the following resources: - Introduction to graph semantics in KQL: https://aka.ms/kusto/graph-overview - Graph operators and functions reference: https://aka.ms/kusto/graph-operators We hope you enjoy this new feature and find it useful for your data analysis needs. As always, we welcome your feedback and suggestions on how to improve KQL and its graph semantics extension. Happy querying! Easily connect to KQL Database from Power BI desktop Power BI desktop just released new ways to easily connect to a KQL database. There are two ways to easily locate your KQL Database: Open the Get Data dialog and select the Microsoft Fabric menu item. Select KQL Databases from the list: Open the OneLake data hub dialog from the main ribbon. Select KQL Databases from the list: Eventstream now supports AMQP format connection string for data ingestion AMQP, stands for Advanced Message Queuing Protocol, is a protocol that supports a wide range of messaging patterns. It's a widely used communication protocol that facilitates efficient and reliable communication among IoT devices and systems. Now in Eventstream, you can create a Custom App source or destination and select AMQP format connection string for ingesting data into Fabric or consuming data from Fabric. Eventstream supports data ingestion from Azure IoT Hub Azure IoT Hub is a cloud-hosted solution that provides secure communication channels for sending and receiving data from IoT devices. With Eventstream, you can easily stream your Azure IoT Hub data into Fabric and perform real-time processing before storing it in Kusto Database or Lakehouse. To add an Azure IoT Hub source, follow these three simple steps: Choose "Azure IoT Hub" as your eventstream source. Give this source a name and enter your Azure IoT Hub details. You may need to enter the share access key from your Azure IoT Hub to establish the cloud connection. On the canvas, select the IoT source and click "Data Preview" to get a preview of the incoming data from your Azure IoT Hub. Improved Eventstream Creation Speed In response to user feedback, we've made significant improvements to the Eventstream creation, enabling you to create an Eventstream item in seconds. Setting up your Eventstream is now faster and smoother than ever before. These improvements save you valuable time and allow you to ingest and transform your data streams more efficiently. Data Factory Data Pipeline The Outlook email & Teams activities in Data Factory Pipelines can now be used in collaborative shared pipeline New pipeline activities are now available as well! If you’ve used the Custom activity type in ADF pipelines in the past, you’ll be very familiar with the new Azure Batch activity in Fabric data pipelines. This will allow you to execute custom executables from Azure Batch for powerful pipeline workflows. We’ve also enabled Azure Functions and KQL scripts as activities in Fabric pipelines. We've updated the Pipeline output monitoring view to focus on simplification, easily representing pipeline run status with customizable output layouts Pipeline lineage now includes lineage for Connections Dataflow Gen2 New Service Principal authentication kind Service Principal has been added as an authentication type for a set of data sources that can be used in Dataset, Dataflow, Dataflow Gen2 and Datamart. Azure service principal is a security identity that is application based and can be assigned permissions to access your data sources. Service principals are used to safely connect to data, without a user identity. Supported data sources include: Azure Synapse analytics Azure SQL database Azure data Lake gen 2 Azure data lake Azure blob storage Web Dataverse SharePoint online Note: service principal is not supported on the on-premises data gateway and virtual network data gateway. Support is planned by the end of 2023. Read more about Service Principal support from the official announcement post. Bug fixes and reliability improvements We continue to listen to your feedback and we’re actively working and making strategic fixes and quality improvements to our service. The following is a short list of the most impactful fixes and improvements delivered in the past couple of weeks. Revert changes for the limit of 50 queries per dataflow. In our previous monthly update we implemented a limit to the number of queries that a dataflow could have. We reverted this limit until further notice due to a number of undesired experiences created, but we will reintroduce this limit again in the near future. If you have dataflows with more than 50 queries currently, we encourage you to start splitting them into multiple dataflows with lower number of queries, as this will give you a head-start when the limit is reintroduced in the future. This documentation article describes several best practices when developing complex dataflows that you may find useful: Best practices for designing and developing complex dataflows - Power Query | Microsoft Learn Multiple fixes and improvements to common error messages and scenarios. Thanks to multiple customers who have reached out to us through our support system and our Community Forum, we’ve been able to address several common issues and improve the error messages that were quite frequent. Some of the error that have been fixed and/or modified to provide more descriptive information are: “The current row is too large to write.” “We cannot convert the value null to type Table.” “Dataflow refresh transaction failed with status Failed” Errors with Dataflow connector when creating linked entities. This issue has been fixed. We want to emphasize our commitment to our customers on improving the product and take your feedback into account in our decision making. We encourage you to engage directly with us through our support channel or through the Community forums. Community New learning path: Implement a Lakehouse with Microsoft Fabric To learn more and get started, check out the Implement a Lakehouse with Microsoft Fabric learning path. That is all for this month! Please continue sending us your feedback and as always, keep voting on Ideas to help us determine what to build next. We are looking forward to hearing from you!
Solved Fabric Community posts are now available in the Fabric Help Pane
The Fabric Help Pane provides Fabric users with a fast and efficient way to access self-help content, allowing them to resolve issues independently or direct them to create a support ticket if further help is required. Following its launch last year, we've heard your feedback to improve the relevance of our self-help resources. We are pleased to announce that now you can find solved posts from Fabric Community discussions relevant to your search in the Fabric Help Pane. To navigate to the help pane, start by clicking on “?” on the top right corner of the screen. Once you are on the help pane, you can search for any issues for which you are trying to find resolution. The search results will return relevant content from both Microsoft Fabric Docs and Community. _Community_posts_are_now_available_in_the_Fabric_Help_Pane If you are only interested in Community posts search results, you can also user the filter and select “Forum Topics” If you have any feedback on the self-content you would like to see in the help pane, please leave us a comment in the Join Discussion section below.
Fabric April 2025 Feature Summary
Welcome to the Fabric April 2025 Feature Summary! This update brings exciting advancements across various workloads, including Low-code AI tools to accelerate productivity in notebooks (Preview), session Scoped distributed #temp table in Fabric Data Warehouse (Generally Available) and the Migration assistant for Fabric Data Warehouse (Preview) to simplify your migration experience. Contents Community & Events Get certified in Fabric – for FREE. Free live learning sessions for Data Engineers General Fabric Copilot and AI Capabilities available on all paid SKUs Data Science Low-code AI tools to accelerate productivity in notebooks (Preview) Low-code AI capabilities in Data Wrangler (Preview) Data Warehouse ALTER Table Drop Column and sp_rename column support in Fabric Warehouse (Generally Available) Session Scoped distributed #temp table in Fabric Data Warehouse (Generally Available) Migration assistant for Fabric Data Warehouse (Preview) OPENROWSET function (Generally Available) BULK INSERT statement (Generally Available) Real-Time Intelligence Fabric special for Kusto Detective Agency: solve the Digibus real-time crisis Azure monitor data sources are now fully integrated with KQL Queryset Improvements to Data Exploration (low-code) experience Eventhouse system and KQL Database overview: in-item monitoring enhancements Eventstream's Real-time Weather Connector SQL database in Fabric New regions supported Backup billing Performance dashboard Terraform support, Rest API & CLI support Integrations Graph database support Data Factory Mirroring Mirroring for Snowflake protected by a firewall(Preview) Closing https://youtu.be/shz68BKiibg?si=iPbDBTTkMu6E59WB Community & Events Get certified in Fabric – for FREE. As part of the Microsoft AI Skills Fest, Microsoft is celebrating 50 years of innovation by giving away 50,000 FREE Microsoft Certification exam vouchers in weekly prize drawings. Enter the sweepstakes now to have the most chances to win a free exam voucher for DP-600 or DP-700. Free live learning sessions for Data Engineers Whether you're new to Microsoft Fabric or building on your existing skills, these sessions, hosted by Microsoft Fabric experts, give you the knowledge and confidence to get certified and take your data engineering career to the next level. Register now - live sessions in English start April 30 th . Available on-demand in Spanish and Portuguese. General Fabric Copilot and AI Capabilities available on all paid SKUs We are thrilled to announce a major update in Microsoft Fabric! Starting today, the SKU requirement for Copilot and AI features will be lowered to F2, making it much more accessible for you to explore, test, and utilize Fabric AI capabilities. This exciting change grants you full access to Copilot in Fabric, Fabric data agents, and Fabric AI Functions—all designed to enhance productivity, uncover insights swiftly, and seamlessly enrich your data. With this update, more teams will have the opportunity to experiment with AI-driven workflows within their existing capacity. It's worth noting that while the smallest SKUs provide full feature access, they will support a more limited number of AI requests due to their smaller capacity size. Nonetheless, this will allow more users to experience the benefits of AI capabilities and improve their workflows. We look forward to seeing the creative ways your team will utilize these AI capabilities to boost your projects and productivity! Data Science Low-code AI tools to accelerate productivity in notebooks (Preview) Fabric notebooks allow you to accelerate your productivity with native AI capabilities like Copilot and AI functions. A new notebook tab devoted to AI and ML tools now provides low-code shortcuts for transforming data with Data Wrangler, training custom models with AutoML, chatting with Copilot, and more. Among the updates is a low-code interface to apply AI functions for seamless LLM-powered data enrichment. Just select one of the functions, choose an input pandas or Spark DataFrame and a target column to transform, and fill in any required parameters. Fabric will produce the code for you. To learn more, refer to the Transform and enrich data seamlessly with AI functions documentation. Low-code AI capabilities in Data Wrangler (Preview) All Fabric notebook users have access to Data Wrangler, a low-code tool with an immersive interface for exploring and transforming pandas or Spark DataFrames. Data Wrangler provides a library of common data-cleaning operations that you can browse and apply seamlessly getting real-time previews and generating reusable code. We have new AI-powered capabilities coming to Data Wrangler later this month: Automated suggestions with rule-based AI: A new set of automated suggestions will analyze your data and use rule-based AI from the Microsoft PROSE team to highlight the most relevant Data Wrangler operations for you. Convert natural language to code with Copilot: Need an operation that you don’t see in Data Wrangler? You can now use Copilot to generate custom code. As with any Data Wrangler operation, you’ll get a preview before applying or discarding it. Use AI to translate custom code from pandas to PySpark: Data Wrangler automatically converts Spark DataFrames to pandas for performance reasons, then translates your applied code back to PySpark when you export it. With GenAI in Data Wrangler, custom code operations will also be translated to PySpark—whether you type them in yourself or generate them with Copilot. Data Warehouse ALTER Table Drop Column and sp_rename column support in Fabric Warehouse (Generally Available) There are two powerful new features in Fabric Warehouse that we are happy to introduce: ALTER TABLE DROP COLUMN and SP_RENAME COLUMN. ALTER TABLE DROP COLUMN effortlessly removes unnecessary columns to streamline storage, boost performance, and improve query efficiency. Cloning a table as of a point in time & time travel to a point in time that is before the table was dropped is not supported. Dropping columns from Lakehouse tables is not a supported scenario. SP_RENAME COLUMN easily renames columns without downtime, making schema adjustments faster and reducing the risk of errors.Columns and Tables are not renamable in Lakehouse. These new features make it easier to maintain a clean and efficient data model, allowing your team to quickly adapt to evolving business needs with minimal disruption. Check sp_rename Microsoft learn and ALTER TABLE (Transact-SQL) - SQL Server for additional details and syntax. Session Scoped distributed #temp table in Fabric Data Warehouse (Generally Available) Are you unable to manage intermediate query results efficiently in your batch jobs? Fabric Data Warehouse users can now create session-scoped #temp tables to handle these results seamlessly. These temp tables can be backed by either Parquet (distributed) or mdf (non-distributed), offering flexible options to cater to different needs. Users can create two types of #temp tables: Non-Distributed Temp Tables (mdf-backed) - These are created using syntax like user tables in Fabric DW, with the key difference being the need to prefix the table name with ‘#’. CREATE TABLE #table_name ( Col1 data_type1, Col2 data_type2 ); Distributed Temp Tables (parquet-backed) - These tables are distributed and created using the following syntax: CREATE TABLE #table_name ( Col1 data_type1, Col2 data_type2 ) WITH (DISTRIBUTION=ROUND_ROBIN); Note: data_type1 and data_type2 are placeholders for the supported data types in Fabric Data Warehouse Data types. For additional details on why we offer two types of temp tables, the scenarios they support, and their limitations, refer to the Session-scoped distributed #temp tables in Fabric Data Warehouse documentation. Start leveraging session-scoped temp tables in Fabric Warehouse to streamline your data processing tasks and enhance your workflow efficiency. Happy querying! Migration assistant for Fabric Data Warehouse (Preview) The Migration Assistant for Fabric Data Warehouse is now in preview. The migration experience is built natively into Fabric and enables Azure Synapse Analytics (Data Warehouse) customers to transition seamlessly to Microsoft Fabric. This new DW migration experience allows users to easily migrate both metadata and data from the source database, automatically converting the source schema to Fabric Data Warehouse, helping with data migration, and providing AI powered assistance. With integrated assessment tools and guided support, this capability simplifies migration, enabling customers to leverage Fabric’s capabilities without the complexity of traditional migrations. The Migration Assistant for Fabric Warehouse streamlines the migration process into four steps: Metadata migration Problem resolution Data copying Connection rerouting Each of these steps are explored in detail in this Migration Assistant for Fabric Data Warehouse (Preview) blog post. For a more comprehensive guide, you can also review the migration assistant how-to article for step-by-step instructions and the Fabric Migration Assistant documentation for more in-depth information. OPENROWSET function (Generally Available) The OPENROWSET function in Fabric Data Warehouse and Fabric SQL endpoint are generally available. The OPENROWSET function enables you to seamlessly read Parquet and CSV files stored in Azure Data Lake Storage and Azure Blob Storage, as it is shown in the following example: SELECT TOP 10 * FROM OPENROWSET( BULK 'https://<storage>.blob.core.windows.net/container/file.parquet' ) With OPENROWSET, you can easily browse files before loading them into the Fabric Data Warehouse, allowing you to inspect the schema before creating the target table. This function provides several valuable features that significantly enhance the data ingestion experience: Referencing custom folder structures – the OPENROWSET function can reference URI patterns by using * (wildcard) and /** (recursive child wildcard) that can match multiple source files that match the same pattern or return all files that are recursively placed under the URI. Reading partitioned data sets – the OPENROWSET function can retrieve partition values from the folder names, which is crucial if you are reading data from hive-style partition structures and return these values in the result set. Reading Parquet complex types – the OPENROWSET function supports complex types such as struct, array, and map, returning them as JSON text for easier manipulation and analysis. Customizing result set scheme – the OPENROWSET function allows you to map the result set columns to the source columns and define the optimal column types for all columns using the WITH clause, providing flexibility in how data is presented and utilized. OPENROWSET supports most of the options available in SQL Server, Azure SQL, and Synapse, facilitating seamless migration and code reuse between these platforms. Ingesting data with CTAS or INSERT SELECT statements – the OPENROWSET function enables you to ingest data using Create Table As Select (CTAS) or INSERT SELECT statements by using the OPENROWSET as a source and allowing you to modify the values from the source values at ingestion time. This is crucial in scenario where you need to modify source data that is not in expected format. The OPENROWSET function will significantly improve your data ingestion experience by enabling you to browse files, transform data during ingestion, and facilitate easier migrations from Synapse, SQL Server, and Azure SQL Database to Fabric Data Warehouse. This powerful functionality ensures that you can handle complex data types, manage partitioned data efficiently, and customize your result set schema to meet your specific requirements, all while maintaining compatibility with existing SQL options. BULK INSERT statement (Generally Available) The BULK INSERT statement in Fabric Data Warehouse is generally available, it enables you to ingest data into a table from the specified file path: BULK INSERT table_name FROM file_url_path The BULK INSERT statement is very similar to the COPY INTO statement and enables you to load data from external storage. The key value of BULK INSERT is that it supports traditional SQL Server and Azure SQL syntax, thus facilitating an easy migration of SQL Server databases to the Fabric Data Warehouse without the need for code changes. Additionally, BULK INSERT supports several traditional options used in SQL Server, such as text/xml format files that are used by bcp tool and importing non-Unicode files with the custom code pages. This compatibility ensures that you can migrate your databases to the Fabric Data Warehouse with minimal code changes to your ingestion code while retaining your existing ingestion logic without altering the input files. By leveraging the BULK INSERT statement, you can maintain your data ingestion workflows and schemas, ensuring a seamless transition to the Fabric Data Warehouse. This feature not only preserves the integrity and structure of the data but also enhances the efficiency of the migration process, reducing the potential for errors and downtime. As a result, businesses can continue to operate smoothly while taking advantage of the advanced capabilities and scalability offered by the Fabric Data Warehouse. Real-Time Intelligence OpenAI plugins for Eventhouse Two new OpenAI plugins are available to generate embeddings and leverage the power of OpenAI models within the Eventhouse context. You can use the plugins to build Retrieval Augmented Graph (RAG) applications or augment your data analysis with OpenAI models. ai_embed_text : Integrates OpenAI embedding models to generate embeddings within KQL. ai_chat_completion: Leverages the power of ChatGPT and other OpenAI models to augment data analysis within the Eventhouse context. Fabric special for Kusto Detective Agency: solve the Digibus real-time crisis This challenge is specifically optimized for onboarding to the RTI platform. You can use it to enhance onboarding for customers and create CTF (Capture the Flag) tournaments to learn with hands-on experience. During this quest, you'll learn KQL and utilize powerful tools including: Eventstream: Process and transform real-time data Eventhouse: Store and query massive datasets Real-Time Dashboard: Visualize critical metrics Activator: Trigger automated responses Put your detective skills to work helping the Digibus Digitown transit company solve its crisis. Follow the clues, analyze the data, and uncover the solution with the power of Real-Time Intelligence. Lucky finishers will win prizes! Don't miss this opportunity to enhance your RTI skills while solving an engaging mystery. Get started with Kusto Detective Agency. Azure monitor data sources are now fully integrated with KQL Queryset KQL Queryset has always supported cross-service queries, but now we’re making Azure data sources even more accessible. Application Insights (AI) and Log Analytics (LA) are now first-class citizens, just like Eventhouse and ADX, providing a more seamless and intuitive experience. With this update, you can: Run cross-service queries between Log Analytics, Application Insights, Eventhouses, and Azure Data Explorer (ADX) native clusters, all connected to the same KQL Queryset. Directly query your Log Analytics workspace or Application Insights resources from KQL Queryset. This makes it easier than ever to explore and analyze data across services without extra configuration. Improvements to Data Exploration (low-code) experience We’re continuing to improve the low-code data exploration experience, making it even easier to analyze data in Real-Time Dashboard tiles and KQL Database tables (in Eventhouse and Real-Time Hub). Here’s what’s new: Hierarchal Columns Pane – easy to use summary of key data characteristics to guide exploration and data manipulation includes: List of participating columns Data types Statistical info (avg, min, max, cardinality etc.) Focus mode – work more efficiently by focusing on either the data visualization or results grid. Right-click actions on grid – quickly copy, export, and perform actions directly from the data grid. _Summary Filter builder with OR conditions –create WHERE statements with more flexible filtering options. Datetime picker control – select exact or relative dates effortlessly when filtering datetime columns. Learn more about data exploration experience here Explore data in Real-Time Dashboard tiles. Eventhouse system and KQL Database overview: in-item monitoring enhancements As part of our ongoing commitment to improving system visibility, performance monitoring, and user experience, we've introduced several enhancements across both the Eventhouse System Overview and KQL Database in-item monitoring pages. Eventhouse system overview enhancements New - Eventhouse ingested rows overtime One of the improvements to the Eventhouse System Overview is the ability to view ingested row metrics directly within the interface. Users can now see the number of rows ingested into each database, offering immediate visibility into data volume and ingestion activity. The feature includes time-based filtering, enabling users to analyze ingestion trends over specific periods. New tab for Top ingested databases Another significant update is the introduction of a dedicated tab for Top Ingested Databases. This new section offers a detailed view of ingestion metrics for each database, including the total number of ingested rows and any ingestion failures (currently only partial failures are reported). The addition of time-based filtering makes it easier to identify patterns and anomalies across specific timeframes. Top 10 ingested databases- improvements The new multi-tab interface displays both the most queried databases and the top ingested databases, improving navigability and offering a clearer view of different performance indicators. Users can also benefit from added cache miss rates that were added to the queries database metrics. Eventhouse details moved to the menu bar To streamline access and maintain design consistency, Eventhouse details have been relocated to the main menu bar. Selecting this option opens a side panel that mirrors the familiar layout used in the Database and Table overview panes. To learn more, refer to the Manage and monitor an Eventhouse documentation. Eventstream's Real-time Weather Connector Last month, we introduced several powerful new connectors for Eventstream in Fabric Real-Time Intelligence. Now, we’re taking it a step further with a hands-on video that shows you how to use one of them: the Real-time Weather connector. In the demo video, we walk through how to easily add the Weather connector to an Eventstream and start streaming live weather data—like temperature, humidity, and wind speed—into Fabric. Whether you’re building real-time dashboards, alert systems, or enriching other streams, weather data adds valuable real-world context to your applications. What you’ll see in the video: A quick overview of the new Eventstream connectors and where to find them Step-by-step demo on adding and configuring the Weather connector Live preview of streaming weather data in action This video is a great starting point if you’re exploring how live data sources can enrich your streaming solutions in Fabric. The Weather connector is especially useful for industries like logistics, agriculture, and retail—anywhere environmental conditions influence operations. Watch the demo to see how easy it is to add real-time weather feed into an Eventstream. Can’t find your data sources, let us know! Send us an email at [email protected] or fill out our survey. h2> Databases SQL database in Fabric We have several new advances to share in SQL Database within Fabric. Continuous innovation is at the heart of our development, outlined are several key enhancements. New regions supported Australia Southeast Italy North Japan East Poland Central WestUS3 are new regions that supports SQL databases in Fabric workloads Backup billing SQL database in Microsoft Fabric offers automatic backups from the moment of database creation, ensuring data protection and recovery. The system makes full backups every week, differential backups every 12 hours and transaction log backups every 10 minutes, providing point-in-time restore capability up to 7 days. While compute and data storage are already included in the Fabric capacity billing model, starting April 1, 2025, backup storage will also be billed. Customers will only be billed for backup storage that exceeds the allocated database size. To learn more, check out the Automatic backups in SQL database in Microsoft Fabric documentation. Performance dashboard The SQL in Fabric dashboard now shows the lead blocking query to allow developers to quickly identify SQL queries that are blocking and impacting other queries thereby disrupting their operational workloads. Link to Video. Terraform support, Rest API & CLI support This capability enables customers to automate, scale, integrate, and govern their SQL databases within Microsoft Fabric, using a declarative approach with Terraform. HashiCorp Terraform, an open-source tool that offers a secure, predictable, and consistent method for deploying and managing infrastructure across multiple cloud environments. This functionality extends the capabilities of Fabric through Infrastructure-as-Code (IaC). To learn more about fabric-terraform-quickstart refer to the documentation. Be sure to check out the blog post Terraform support for Fabric GA for a more information. Integrations Fabric data pipelines will now support Fabric SQL database as a data source for Stored Procedure and Script activities, allowing users to just pick a database rather than having to enter connection information. Graph database support The query editor in SQL database in Fabric now have T-SQL support for graph databases. This feature enables the modeling of many-to-many relationships. You can create a graph database with nodes and edges and utilize the new MATCH clause to identify patterns and navigate through the graph. Learn more about how to Create a graph database and run some pattern matching queries using T-SQL from our documentation. Data Factory Mirroring Mirroring for Snowflake protected by a firewall (Preview) You now can mirror Snowflake protected by a firewall. Using either the VNet data gateway or the on-premises data gateway for mirroring is available. The data gateway facilitates secure connections to your source databases through a private endpoint or from a specific private network. Learn more about Mirroring for Snowflake from the Microsoft Fabric Mirrored Databases from Snowflake documentation. Closing We hope that you enjoy the update! Be sure to join the conversation in the Fabric Community and check out the Fabric documentation to get deeper into the technical details. As always, keep voting on Ideas to help us determine what to build next. We are looking forward to hearing from you!
